the trading volume impact of local bias: evidence from a ... & weber...the trading volume impact...

Electronic copy available at: http://ssrn.com/abstract=1527579Electronic copy available at: http://ssrn.com/abstract=1527579

The Trading Volume Impact of Local Bias: Evidence from a

Natural Experiment

Heiko Jacobs and Martin Weber∗

June 1, 2011

Review of Finance, forthcoming

Appendix attached

Abstract

Exploiting regional holidays in Germany as a source of exogenous cross-sectional variation in in-vestor attention, we provide evidence that the well-known local bias at the individual level materiallyaffects stock turnover at the firm level. Stocks of firms located in holiday regions are temporarily strik-ingly less traded than otherwise very similar stocks in non-holiday regions. This negative turnovershock survives comprehensive tests for differences in information release. It appears particularly pro-nounced in stocks less visible to non-local investors, and for smaller stocks disproportionately drivenby retail investors. Our findings contribute to research on local bias, trading activity, and investordistraction.

Keywords: Local bias, trading activity, investor distraction, holiday effect, natural experiment

JEL Classification Codes: G12, G14

∗Heiko Jacobs is from the Lehrstuhl fur Bankbetriebslehre, Universitat Mannheim, L 5, 2, 68131 Mannheim. E-Mail:

[email protected]. Martin Weber is from the Lehrstuhl fur Bankbetriebslehre, Universitat Mannheim, L

5, 2, 68131 Mannheim, and CEPR, London. E-Mail: [email protected]. Mark Seasholes and an anonymous

referee provided many constructive suggestions that considerably improved the paper. We thank seminar participants at

the 17th Annual Meeting of the German Finance Association (DGF), at the 9th Cologne Colloquium on Financial Markets,

at the 14th Conference of the Swiss Society for Financial Market Research (SGF), and at the University of Mannheim for

valuable comments. We would like to thank Alexander Hillert for excellent research assistance. Financial support from the

Deutsche Forschungsgemeinschaft (SFB 504) and the Hamburg Stock Exchange is gratefully acknowledged.

1

Electronic copy available at: http://ssrn.com/abstract=1527579Electronic copy available at: http://ssrn.com/abstract=1527579

1. Introduction

By now there is ample evidence that both private and professional investors have a strong

preference for trading stocks of locally-headquartered firms. But is this so-called local bias

strong and pervasive enough to matter for the cross-section of stock turnover at the firm

level? To answer this question, we run a natural experiment in the German stock market.

Germany has several holidays which are observed only in some of its 16 states. While

these holidays have a religious origin, they materially influence public life as a whole.

Authorized by law, they are characterized by a limit or ban on work and official business

(but not exchanges). Previous research (e.g. DellaVigna and Pollet (2009), Hong and Yu

(2009), and Frieder and Subrahmanyam (2004)) and casual evidence suggest that both

private and professional investors in holiday regions tend to be temporarily distracted and

thus to often refrain from actively participating in the stock market on such days.

This exogenous variation in investor attention along a geographical line would not have

implications for the cross-section of abnormal firm-level trading activity if investors traded

the market portfolio. Only the aggregate level of trading volume might then be affected

(e.g. Lo and Wang (2000)). However, the introduction of local bias gives rise to a cross-

sectional hypothesis untested so far: Stocks of firms located in holiday regions (in the

following referred to as holiday firms) should, all else equal, exhibit a more pronounced

negative shock in trading activity than stocks of firms located in unaffected regions (in the

following referred to as non-holiday firms). An advantage of the German setting is that

both samples are similar and thus satisfy the requirements of a natural experiment: They

are broadly homogenous with respect to e.g. the number of firms, industry composition,

typical firm size, average stock risk-return profiles or (unconditional) turnover properties.

Similar findings apply to important characteristics of individual investors.

Consistent with our line of reasoning, we indeed find that holiday firms are (only) tem-

porarily strikingly less traded, both in statistical and economic terms. The negative shock

in turnover relative to non-holiday firms ranges roughly from 10% to 20%. It is not affected

by the inclusion of various control variables or several changes in methodology.

To the extent that news arrival triggers abnormal trading, one might be concerned that

2

our findings could be driven by a temporary change in the cross-section of information

release. Note, however, that the vast amount of firm-relevant news on a market, industry,

style or other aggregated levels should not be affected by regional holidays. It is arguably

only the structure of idiosyncratic firm-specific news, generated in or near a firm’s head-

quarter, which might potentially be affected. Digging deeper, we explore this news-based

explanation of our findings from five perspectives. From a firm perspective, we analyze

shocks in the release of corporate news. From a market perspective, we study shocks in

the idiosyncratic component of stock returns. From an investor’s viewpoint, we explore

shocks in the search frequency for firm names in Google. From an analyst perspective,

we study shocks in the cross-section of stock recommendations. From a media point of

view, we analyze shocks in press coverage. Overall, these tests (only) sporadically point

to significant differences in information release. Thus, we cannot rule out the possibility

of lower information intensity for holiday firms contributing to our results. However, we

believe it is justified to argue that information effects are unlikely to fully explain the

magnitude and robustness of the findings we document.

In line with a local bias explanation and the investor recognition hypothesis of Merton

(1987), the regional holiday effect is particularly pronounced for firms less visible to non-

local investors. Market capitalization, idiosyncratic risk and residual media coverage are

used as proxies for visibility. Finally, we study daily trading patterns of about 3,000

private investors from a German online broker. Consistent with implications of previous

research, individual investors seem to disproportionately cause the negative turnover shock

in smaller firms, in which their localized trading is concentrated.

Our study contributes to the literature in several ways. First, while prior research shows

that investors are biased towards the stocks of nearby firms, we identify scenarios in which

these individual preferences are strong and pervasive enough to materially affect the cross-

section of stock turnover. To our knowledge, our novel approach thereby provides the first

non-US evidence of local bias affecting market outcomes.

Second, our findings help to better understand determinants of stock-level trading volume,

which plays an essential role in much research on liquidity, return predictability, behavioral

finance or information asymmetries. For example, Hong and Stein (2007) note that “many

of most interesting patterns in prices and returns are tightly linked to movements in

3

volume” (p. 111). At the same time, empirical evidence on the drivers of its substantial

variation both in the cross-section and time-series is scarce (see e.g. the discussions in Gao

and Lin (2010), Statman et al. (2006) or Chordia et al. (2007)). We add to this literature by

uncovering cross-sectional regularities related to firm location, firm visibility, and investor

clienteles.

Third, a growing body of research builds on the idea of limited attention, whereby in-

vestors process only a subset of publicly available information due to attention capacity

constraints. A challenge for empirical work is the identification of a suitable proxy for

investor distraction. For example, Hou et al. (2009) rely on down market periods, while

Hirshleifer et al. (2009) employ the number of competing earnings announcements. In a

scenario related to ours, DellaVigna and Pollet (2009) analyze the market response to

earnings announcements on Fridays, when, as they argue, investor inattention is more

likely. Our findings highlight the role of regional holidays as a promising proxy for limited

attention. We identify scenarios which seem to cause distraction of an important sub-

set of investors, leading to market frictions in trading activity along a geographical line.

Moreover, we explore which firms and investor groups tend to be most affected.

The remainder of this paper is organized as follows. Section two discusses related research

and develops our hypotheses. Section three describes sample characteristics. Section four

contains the event study and explores alternative interpretations of our findings. Section

five analyzes determinants of the regional holiday effect. Section six concludes.

4

2. Related Literature and Hypotheses

By now, there is extensive and robust evidence for local bias on an individual level.1

However, research exploring its implications for return and volume patterns is still at the

beginning and moreover limited to the US market. Pirinsky and Wang (2006) document

an excessive comovement of local stock returns, which they attribute to correlated trad-

ing of local residents. Building on investors’ consumption smoothing motives, Korniotis

and Kumar (2010) argue that stock returns contain a predictable local component. The

findings of Hong et al. (2008) suggest that, in the presence of only few local firms compet-

ing for investors’ money, share prices of spatially close firms are driven up by the excess

demand of proximate residents. In a current study based on intra-day data, Shive (2011)

exploits large power outages to study the effect of local investor clienteles on pricing effi-

ciency. Her study provides evidence that informed local investors play an important role

in information processing and price discovery.

To the best of our knowledge, only two papers focus on the impact of local bias on firm-

level turnover. Loughran and Schultz (2004) show, among other pieces of evidence, that

the time zone in which a firm is headquartered triggers intraday trading patterns in its

stock. Loughran and Schultz (2005) demonstrate that rural stocks are less liquid than

urban stocks, which they attribute to the latter being local and thus visible to more

potential investors. They conclude that “much remains to be done on geography and

asset pricing” (p. 363). We aim at taking a step in this direction by exploiting holidays

which are observed only in some areas of Germany. In our baseline analysis, we focus

on All Saints’ Day as well as on Epiphany. All Saints’ Day, celebrated on November

1, is legally recognized only in the states of Baden-Wuerttemberg, Bavaria, Northrhine-

1Heterogeneous findings suggest that both informational and behavioral factors are likely to drive local bias. Studies

attributing this behavior to a preference for investing into the familiar, to the pronounced visibility of local stocks or to

incorrectly perceived information advantages include e.g. Bailey et al. (2008), Grinblatt and Keloharju (2001), Huberman

(2001), Seasholes and Zhu (2010), and Zhu (2003). Papers arguing in favor of superior locally generated information include

e.g. Baik et al. (2010), Bodnaruk (2009), Coval and Moskowitz (1999), Coval and Moskowitz (2001), Feng and Seasholes

(2004), Ivkovic and Weisbenner (2005), and Massa and Simonov (2006). Moreover, recent studies of Brown et al. (2008),

Hong et al. (2004), Hong et al. (2005), Ivkovic and Weisbenner (2007), and Shive (2010) show that local social interaction

and neighborhood word-of-mouth effects strongly affect investment decisions. Local bias has been shown to be robust

across countries, investor subgroups and sample periods. For the German market, combined findings from e.g. Dorn and

Huberman (2005), Dorn et al. (2008), Hau (2001), and this study suggest that, in the overall picture, German investors

pose no exception.

5

Westphalia, Rhineland Palatinate, and Saarland. Epiphany, celebrated on January 6, is a

legally recognized holiday only in the states of Baden-Wuerttemberg, Bavaria, and Saxony-

Anhalt. There are more regional celebrations in Germany (see the appendix, which is

available as supplemental material on the journal’s homepage www.revfin.org and from

the publisher web site rof.oxfordjournals.org). We partly rely on these holidays in later

tests. However, focusing on Epiphany and All Saints’ Day yields the most attractive event

study properties: It is a yearly event which splits the market in two large disjunct groups

with similar characteristics (see section 3 for details).

How holidays in general affect (in particular private) investors’ trading behavior is an

empirical question. On the one hand, one might expect increased trading activity, as in-

vestors may have more time to engage in the stock market. On the other hand, one might

expect decreased trading activity, as investors could indulge in vacation activities and

thus refrain from participation in the market. Indeed, previous work supports this second

line of reasoning. Frieder and Subrahmanyam (2004) show that turnover drops during

nationwide holidays. Hong and Yu (2009) provide evidence of aggregate trading activity

in international stock markets (including Germany) being lower during summer holiday

periods, which they dub a “gone fishin’ effect”. This seasonality in turnover seems to

be caused by both private and professional investors. DellaVigna and Pollet (2009) re-

port that trading activity immediately after earnings announcements made on Fridays is

comparatively low, as investors tend to be absent-minded due to the upcoming weekend.

With regard to the German setting, the idea of investors being temporarily distracted is

backed up by anecdotal evidence from leading papers and news services.2 Combined with

local bias, this type of limited investor attention makes novel predictions. Specifically,

if investors tend to heavily overweight local stocks in their investment decisions, then a

large, geographically concentrated subset of holiday-distracted investors might temporar-

2For instance, Die Welt (May 27, 2005), Financial T imes Deutschland (June 12, 2009), Tagesspiegel (June 12, 2009),

Stuttgarter Zeitung (May 8, 2007; May 24, 2008), DPA (May 25, 2001), and Dow Jones (June 1, 2007) all report that

many investors, both private and professional, stay out of the market on regional holidays and corresponding bridge days.

Other articles indirectly point to (primarily retail) investor distraction. For example, Frankfurter Rundschau (October 30,

2004) and Die Welt (November 2, 2004) report that non-holiday states profit from increased holiday tourism. AHGZ (May

12, 2007), a magazine for the hotel and catering sector, states that retail sales volume is higher around regional holidays.

Spiegel Online (June 14, 2006) and ddp (June 8, 2009) point to the danger of traffic jams due to the large number of people

on a short holiday. Sueddeutsche Zeitung (October 31, 2000) writes about massive obstructions of traffic near graveyards

on All Saints’ Day, on which it is custom to honor the deceased.

6

ily change the cross-section of stock turnover:

Hypothesis 1: Due to local bias, trading activity during regional holidays will be significantly

lower for firms in holiday regions.

This hypothesis is consistent with the trading volume implications of the habitat-based

model of comovement in Barberis et al. (2005). Similarly, in the model of Merton (1987),

investors are aware only of a subset of the stock universe. Consequently, the demand for

each stock depends on its shadow cost of information. In equilibrium, firms recognized by

less investors, will, all else equal, have fewer shareholders taking relatively large positions.

It seems plausible to assume that investor recognition of a firm is negatively correlated

with geographical distance. We thus expect the impact of local investors to be particularly

strong for firms which are hardly visible to remote investors:

Hypothesis 2: The negative turnover shock will be more pronounced for those local firms

which are less recognized by non-local investors.

We also explore whether there are differences across investor types, which empirical find-

ings assess to be likely. The aforementioned evidence of limited stock market participation

during holidays appears to hold particularly true for private investors. At the same time,

retail stock ownership tends to be more exposed to local bias than institutional stock hold-

ings (e.g. Grinblatt and Keloharju (2001)). Small firms have been shown to be investment

habitats of retail investors (e.g. Dorn et al. (2008), Kumar and Lee (2006)), whose local

bias is particularly concentrated in these stocks (e.g. Zhu (2003)). Thus, traces of retail

investor behavior in firm-level turnover should be most easily detected in small stocks.

Combined with the observation of Goetzmann and Kumar (2008) that those investors

who trade excessively are particularly locally biased, the rich set of findings suggests:

Hypothesis 3: The negative turnover shock in smaller firms will be disproportionately

caused by individual investors.

7

3. Sample Characteristics

We follow the consensus in the literature on local bias and use a firm’s headquarter as

a proxy for its location. Our initial sample consists of the common stocks of all firms

headquartered in Germany which have been listed on a German stock exchange at some

point between June 13, 1988 and January 15, 2009.3 The lower bound is determined

by the availability of the daily number of shares traded. The upper bound is meant to

maximize the sample size by the inclusion of Epiphany (January 6) in 2009. The data

is then subjected to a three-stage screening process.4 This leaves a final sample of 792

stocks, for which the appendix (available on the web as supplemental material) provides

descriptive statistics at a weekly frequency. The mean (median) firm is in our sample for

556 (515) weeks, has an average market capitalization of 1,148 (123) million Euro, and

has a weekly turnover of 1.42% (0.93%). There is large cross-sectional and considerable

time-series variation in turnover, which again motivates the exploration of local bias as a

potential driver of firm-level trading activity.

Please insert figure 1 and table 1 about here

Table 1 shows summary statistics for event study samples. Several findings highlight

advantages of the German setting. First, both the treatment (holiday) and the control

(non-holiday) groups form large portfolios. Second, their composition does not seem to

differ much. For example, median firms have about the same market capitalization and

comparable average stock returns. Industry concentration, as computed from Herfindahl

3See the appendix, which is available on the web as supplemental material, for an overview of all data sets used in

this study. For the holidays analyzed here, the Frankfurt stock exchange has been open over the whole sample period,

while stock trading at the regional exchanges in Germany started in 2000. This is unlikely to influence our results for three

reasons. First, for all sample stocks, the primary exchange from which Datastream obtains its default prices turns out to

be the Frankfurt stock exchange. Second, inferences remain unchanged if we restrict our analysis to those stocks which are

exclusively traded on the Frankfurt stock exchange. Third, results are robust across time. In particular, they also hold for

the subperiod 2000-2009 (see sections 4.2 and 4.3 for details).

4First, adjusted and unadjusted daily closing prices, market capitalization, book values, the number of daily shares

traded, the number of total shares outstanding, adjustment factors as well as industry membership have to be available

via Datastream. Second, we conduct the tests suggested by Ince and Porter (2006). Third, to assure that our analysis is

not contaminated by very small and illiquid stocks, we exclude securities if their mean market capitalization is less than 10

million Euro or if the 5th percentile of their unadjusted prices is less than 1 Euro. The main results do not change if we use

the sample after step two, which contains 1,071 stocks.

8

indices based on Datastream Level 2 industry classification, is very similar. The appendix

shows that also industry composition appears broadly comparable. Similar findings ap-

ply to the fraction of large firm observations. Third, the time-series properties of local

turnover indices show a remarkably similar behavior, even in the tails of the distribution.

Fourth, an eyeball analysis of figure 1, which shows the geographic distribution of sample

firms, reveals that firm location in Germany tends to be less concentrated than in the

predominantly used US samples (e.g. Ivkovic and Weisbenner (2005)). Fifth, not only

firm-level variables, but also individual investors’ characteristics seem comparable. This

is suggested by calculations based on data of the German SAVE study5, a comprehen-

sive panel survey designed to provide representative information on the financial situation

and relevant socio-psychological traits of German households. For example, households’

propensity to participate in the stock market, investors’ risk taking behavior and eco-

nomic expectations, their financial literacy and use of financial advice, or the influence of

social contacts on financial decision-making is similar in control and treatment groups.

With regard to typical US samples of previous local bias studies, Seasholes and Zhu (2010)

highlight a cross-sectional geographic sampling error, which they argue to potentially lead

to incorrect conclusions. Taken together, the German setting seems to suffer less from

this selection bias. Instead, portfolios are broadly diversified, homogeneous in several

dimensions and thus seem particularly suitable for the following natural experiment.

4. Event Study

4.1 METHODS AND BASELINE RESULTS

In order to quantify the impact of localized trading, one needs to define a measure of

trading activity. We focus on firm turnover as “turnover yields the sharpest empirical

implications and is the most natural measure” (Lo and Wang (2000), p. 12). As turnover

is naturally skewed, we use its natural logarithm in the following calculations. In the

regression setting targeted at testing hypothesis 1, the dependent variable TOi,t is the

daily turnover of firm i on a regional holiday at time t. We consider each year from 1988

5See the appendix for a detailed analysis. For an overview of the SAVE study, see Boersch-Supan et al. (2009).

9

to 2009 in which the holiday falls on a trading day. For All Saints’ Day (Epiphany), this

results in 16 (14) years with a total of 6,485 (5,657) observations.

During regional holidays, market turnover in general tends to be lower. The average daily

turnover of a value-weighted (equal-weighted) turnover index during the whole sample

period is 0.42% (0.20%). On Epiphany, these numbers decrease to 0.36% (0.14%), on All

Saints’ Day to 0.29% (0.13%). However, we are not interested in changes in trading activity

per se, but in potential cross-sectional differences between holiday and non-holiday firms.

Thus, the independent variable of interest is the holiday region dummy Holi,t that equals

one if a firm’s headquarter is located in a holiday region and zero otherwise. The null

hypothesis is that the dummy should not have any significance.

To isolate the holiday effect, it is essential to control for the expected level E[TOi,t]

of turnover. To assure robustness, we rely on two models widely employed in previous

research. Model 1 accounts for firm-specific average turnover in the pre-event period (e.g.

Chae (2005)). In the baseline analysis, the expected firm turnover is calculated as the

natural logarithm of the average turnover over t-20 to t-2. Model 2 controls for both

market-related and firm-specific volume by adopting a “turnover market model”(e.g. Tkac

(1999)). To this end, for t-60 to t-2, turnover for each firm is regressed on a market-

wide, value-weighted turnover index TOm,t. Using the coefficients from the time-series

regression, expected turnover is then given by

E[TOi,t] = αi + βiTOm,t. (1)

As current firm-level turnover might be related to current stock return (e.g. Chordia et al.

(2007)), we include two control variables. Ret+,i,t represents the event day stock return if

positive and zero otherwise.6 Ret−,i,t is defined analogously. This distinction is motivated

by possible asymmetric effects caused by short-selling constraints or the disposition effect,

which have been shown to affect localized trading (e.g. Grinblatt and Keloharju (2001)).

It has also been documented that turnover is influenced by lagged stock returns (e.g.

Statman et al. (2006), Glaser and Weber (2009)). This effect should be captured at least

partly by our measures of expected turnover. To more fully control for recent past returns,

6However, our results do not change if we only include the lagged return or if we do not add return-related control

variables at all. Moreover, as shown in section 4.2, inferences are the same when including interaction terms to allow for a

different impact of returns on holiday firm turnover.

10

we include two analogous variables (Ret+,i,t−1 and Ret−,i,t−1) for the pre-event day return.

The return controls might also be regarded as crude proxies for news or rumors, which

could affect turnover. In section 4.3, we comprehensively test for differences in information

release between holiday and non-holiday firms.

In our basic regression setting, we employ a Fama-MacBeth approach, combined with the

method of Newey and West (1987). We implement the following cross-sectional model in

each year and use the resulting time-series of coefficients to assess their significance:

TOi,t = β0,t + β1,tE[TOi,t] +5∑

k=2

βk,tReturnControlk,i,t + β6,tHoli,t + ϵi,t (2)

Table 2 shows the findings for the Epiphany sample and the All Saints’ Day sample,

respectively. Displayed are results from three regression specifications, which differ in the

dependent variable. The baseline regression uses firm-specific turnover at the day of the

holiday (TOi,t), the others use the day preceding and following the holiday, respectively.

Please insert table 2 about here

The holiday region dummy attains a highly negative coefficient in all specifications. For

both the Epiphany and the All Saints’ Day sample, and for both models of expected

turnover, the coefficient is strongly significant at the one percent level. The upper bounds

of the 95% confidence intervals are all well below zero. Moreover, from an economic

perspective, the effect is quite large: The pure holiday-induced abnormal drop in volume

ranges from roughly 10% to slightly over 20%. Additionally, results are robust across

time: In the Epiphany sample, the holiday region dummy is negative in each year; in the

All Saints’ Day sample, it attains a negative coefficient in about 80% of the observations.

Finally, the holiday effect can, for the most part, only be identified at the day of the holiday

itself. On the day before the holiday, there is no negative shock in trading activity; on the

day after, there is some evidence, which, however, is much weaker than on the date of the

holiday itself.7 In sum, the findings so far support hypothesis 1.

7As unreported findings suggest, the effect on the day after the holiday might at least partly be attributable to the

impact of bridge days as well as the end of Christmas holidays, which varies both across time and states, respectively. This

seems also consistent with the anecdotal evidence given in footnote 2.

11

4.2 ROBUSTNESS CHECKS

The main results from a variety of sensitivity tests are summarized in table 3.


Our test specification might be misspecified in the sense that it may lead to a spurious

positive factor loading on the holiday region dummy on average, irrespective of an actual

holiday event. We therefore implement a “placebo treatment”: For each model specifica-

tion and each holiday sample, we randomly select 500 days (excluding the period from

t-1 to t+1, where t denotes the holiday) and, for each of these pseudo events, run the re-

gression as given in Equation (2). Mean and median factor loadings on the holiday region

dummy are given in panel A. In all specifications, they are virtually zero.

There is arguably some element of arbitrariness in the length of the pre-event period in

both models of expected turnover. Therefore, we experimented with intervals from 10 to

100 trading days. Panel B verifies that inferences remain the same.

It might be possible that the importance of the return controls varies between holiday

and non-holiday firms. We thus interact all return variables from the baseline regression

with the regional holiday dummy. It turns out that none of them is significant. Panel C

shows that the importance of the holiday region dummy remains unaffected.

One might be concerned that the results could partially be driven by a disproportionate

number of holiday firms whose stocks are not traded at the event day. Our findings might

then not reflect a broader phenomenon, but rather be attributable to outliers. We thus

repeat the analysis discarding all stocks with zero trading volume. However, as shown in

panel D, this exercise rather strengthens our results.

Panel E shows results when using raw (instead of logarithmized) turnover. In all specifi-

cations, the holiday effect is significant at the 1% level. Moreover, it keeps its economic

significance. For the mean (median) firm the results indicate a pure regional holiday-

induced drop in daily trading volume of roughly 200,000 (more than 20,000) Euro.

12

Residuals of a given firm might be correlated across years, potentially leading to biased

standard errors. We thus follow a suggestion of Petersen (2009) by pooling all firms with

non-zero turnover, adding year dummies and clustering standard errors by firm. As shown

in panel F, findings are robust to this alternative econometric specification.

Legally recognized regional holidays are observed on a state level. Thus, our interpretation

rests on the idea of states being an appropriate classification of the preferred investment

habitats of local investors. While similar concepts have been proven fruitful in US studies

(e.g. Hong et al. (2008), Korniotis and Kumar (2010)), it is clearly only a noisy proxy.

Note, though, that this works against detecting a regional holiday effect: If local investors

tilted their trading towards stocks of local firms irrespective of state borders, then it

would be hard to identify differences in trading activity between two neighboring states.

In an attempt to use a classification scheme with a more pronounced socio-economic

background, we repeat our analysis building on metropolitan areas as defined by the

Conference of Ministers for Spatial Planning.8 Some areas span more than one state,

whereas some states contain more than one metropolitan region. Panel G verifies that the

coefficient is sporadically estimated even marginally more precisely, possibly pointing to

the true impact of localized trading being stronger than reported.

We also study turnover shocks on Corpus Christi as the third legally recognized regional

holiday. It is celebrated in the states of Baden-Wuerttemberg, Bavaria, Hesse, North

Rhine-Westphalia, Rhineland-Palatinate and Saarland, at the Thursday 60 days after

Easter Sunday. Stock market trading on this day started not before 2000, which results in

a total of 5,078 firm-level observations distributed over nine yearly observations. Panel H

verifies that our findings hold also in this case. The shock in turnover is highly significant,

and estimated to be close to 20%. The setting for Corpus Christi is, apart from the shorter

sample period and the fixed day of the week, not conceptually different from Epiphany

and All Saints’ Day. Including all holidays in the remaining tests increases the sample size

and ensures that we consider each regional holiday in Germany for which requirements

on a meaningful event study are met.

8This classification identifies eleven metropolitan regions in which roughly 70% of the German population and 84%

of sample firms are located. http://www.eurometrex.org defines these areas as “larger centres of economic and social life”

containing “core business, cultural and governmental functions”. We only consider areas clearly belonging either to a holiday

or a non-holiday region. This leaves a total of 5,416 (4,350) observations for the All Saints’ Day (Epiphany) sample.

13

However, if our results were representative of a widespread localized trading phenomenon,

then we might also detect similar patterns in related scenarios such as Carnival. While

there is no official holiday, representative surveys reveal that Carnival is prominent in some

(mostly southern and western) regions, but rather unpopular in other (mostly northern

and eastern) areas of Germany.9 Despite the lack of clear-cut separation between affected

and non-affected regions, we run an analogous analysis for Carnival Monday, on which

most parades are held. Panel I provides evidence supportive of our line of reasoning.

4.3 DIFFERENCES IN INFORMATION INTENSITY?

A key concern to a local bias story is that holiday firms might simply release less in-

formation than otherwise comparable non-holiday firms. To the extent that this triggers

rebalancing trades or increased differences of opinion, it might partly explain our findings.

As an intuitive and rather informal first approach to explore the possibility of such an

information effect, we compare the fraction of corporate news released around the holiday.

To this end, we rely on firm-specific news stories published by DGAP, a German news

agency, from January 2000 to January 2009. These news include time-stamped ad hoc

disclosures, by which German firms are forced to publish new value-relevant information

immediately. We manually collect these disclosures for each sample firm. The database

additionally covers a broad range of other news, such as directors’ dealings or business

reports. Since data retrieval is labor intensive, we gather these corporate news for half of

sample firms, which we randomly select. The following test is based on this subsample.

We create a dummy variable that states for each firm and each day whether corporate

news or ad hoc disclosures have been released. Then, for each holiday, and separately for

the holiday and the non-holiday sample, we compute the fraction of all news attributable

to a short window around the holiday (t-1 to t+1). After that, we compute an odds ratio

9We here rely on survey results published in the magazine “Daheim in Deutschland” (by Reader’s Digest), February

2010. Our classification is based on the fraction of individuals stating to actively participate in carnival celebrations. The

areas of Hesse, Rhineland Palatinate, Saarland (roughly 30%), Bavaria (27%), Baden Wurttemberg (25%) and North Rhine-

Westphalia (24%) serve as a treatment group. The remaining regions have participation rates between 10% and 19% and

thus serve as a control group. A related classification scheme based on the relative popularity of carnival clubs leads to

similar results. Data for this analysis is provided by “Bund Deutscher Karneval”, the umbrella organization of several

thousand German carnival clubs.

14

by dividing the percentage obtained for the holiday sample by the percentage obtained for

the non-holiday sample. If holiday firms released temporarily less news, we would expect

values persistently well below one. However, the odds ratios are 1.05 for Epiphany, 1.02 for

All Saint’s Day and 0.84 for Corpus Christi, pointing against a widespread drop in news

release. As later sections of this study reveal that firm size is an important determinant of

the drop in trading activity, we determine whether this might be due to differences in news

release. Specifically, we repeat the analysis separately for large and small stocks, split by

the median of market capitalization at the beginning of the year. Around Epiphany, there

are no marked differences. Around All Saint’s Day, small holiday firms appear to release

relatively more news than large holiday firms. Around Corpus Christi, this picture partly

reverses. In sum, there is no clear pattern.

For deeper insights, we test more rigorously for differences in news arrival from four

further perspectives. Specifically, we study cross-sectional shocks with regard to abnormal

price movements, with regard to the degree of analyst coverage, with regard to investors’

internet search behavior as well as with regard to firms’ media exposure. In the following,

these tests, whose main results are presented in tables 4 and 5, are described in detail.

Please insert table 4 and table 5 about here

Firm-specific news are likely to affect the magnitude of abnormal returns. Firm-specific

information should manifest itself in an increased importance of the idiosyncratic com-

ponent of the firm’s daily stock return. On the other hand, if there is hardly any new

information, then the return should primarily be driven by the stock’s exposure to perva-

sive well-known risk factors. Thus, if there was indeed temporarily less news for the typical

holiday firm, we would expect its absolute abnormal return to be considerably lower than

during some control period on average. For the typical non-holiday firm, however, there

should be no or at least not as much of a difference. A benefit of this approach is that

shock variables can be computed continuously, providing data for each firm on each day.

This overcomes the problem that official news coverage of a given firm may be sporadic,

even though there might be rumors, speculation or private information investors react

on. To formalize the cross-sectional prediction as sketched above, we employ the following

procedure. First, for each firm and each day, we compute the abnormal stock return. By

15

employing both a market model and a Carhart (1997) four factor model, we follow stan-

dard event study methodology; due to very similar findings, only the findings from the

latter model are reported. The four factor model is based on German data and includes

the market, size and value factors in the spirit of Fama and French (1993) and the mo-

mentum factor as constructed in Carhart (1997). The appendix provides more detailed

information about the construction of the factors. Second, for each firm, we compute the

difference between the absolute abnormal return on the day of the holiday (=t) and the

average absolute abnormal return in some control period. We here rely on the period from

t-5 to t+5 (excluding t), but results are not sensitive to this choice. The resulting variable

has the interpretation of an unexpected change in the relative importance of idiosyncratic

stock return factors. Third, for both the holiday and the non-holiday sample, we rank firms

based on this shock variable. We take the cross-sectional median for both samples to get

an estimate of the shock for the typical firm.10 Fourth, we compute the cross-sectional

difference between the median shock for the holiday sample and the median shock for

the non-holiday sample. A news-based explanation of our findings would predict values

significantly below zero, as the shock in the relative importance of firm-specific return fac-

tors for the typical holiday (non-holiday) firm should be more (less) negative. We repeat

the procedure in each year. Finally, a bootstrap approach11 is used to test whether the

average of the resulting time series of differences is statistically distinguishable from zero.

However, panel A of table 4, which reports results for the Epiphany, All Saints’ Day as

well as Corpus Christi sample, shows that this not the case. The only slightly significant

event is on the day of Epiphany, where, from an economic perspective, the resulting re-

turn difference appears small. For all other holidays, differences are very close to zero and

insignificant, implying that in most cases shocks in abnormal returns do not differ much

between holiday and non-holiday firms. Pooling observations does not lead to different

10The appendix provides more details about the distribution of shock variables. It verifies that findings are qualitatively

similar when relying on the mean (instead of the median) of the winsorized cross-section. It also shows that extreme return

events are only slightly more frequent for non-holiday firms.

11The comparison of shock variables results in a holiday-specific time series of differences between holiday and non-

holiday firms. We use this data to simulate 10,000 pseudo time-series of the same length as the original sample by randomly

drawing values with replacement. Averaging values separately for each pseudo time-series yields 10,000 pseudo estimates of

the difference in median shock variables. Finally, we assess whether the value obtained from the averaged original time-series

is reliably negative by computing the fraction of simulated estimates that take on values below zero. For a discussion of

simulations in event studies, see e.g. Lyon et al. (1999).

16

conclusions. Moreover, there are no persistent differences for large and small stocks, again

split by the median of market value.

Our second test is inspired by Da et al. (2011) and based on cross-sectional shocks in search

frequencies for firm names in Google. The application “Google Insights for Search” allows

to construct standardized time-series of terms entered in the internet search engine. Data

is available on a daily basis from January 2004 on. Computing shocks in search volume

might be regarded as a possibility to quantify unexpected changes in revealed (and thus

direct) focus to individual firms, induced by some external stimulus. In this sense, changes

in the query frequency of a firm name12 appear a promising way of capturing shocks in

the arrival of firm-specific news or rumors. For example, Da et al. (2011) report a positive

correlation between search volume shocks and traditional proxies for information release,

such as extreme returns and news stories. The authors show that internet search volume

even often leads alternative measures of news arrival. We thus construct a measure of

unexpected search behavior for each firm based on daily data. It is defined as the difference

between the search frequency during the holiday (=t) minus the average frequency over

t-10 to t-2, divided by the standard deviation in this pre-event period. We then pool

observations and regress the shock variables on a holiday region dummy in addition to

controls for years and industries. We do so for the sample of all firms, of large firms, and

of small firms. Panel B of table 4 shows that all holiday region dummy coefficients are

insignificant, both separately and jointly, pointing against a news-based story.

Our third analysis focuses on the large effort of analysts in collecting, processing and dis-

seminating information (e.g. Womack (1996)). We are interested in whether aggregated

analyst coverage during regional holidays differs from coverage in a nearby benchmark pe-

riod. Specifically, we concentrate on the number of daily analyst recommendations issued,

and determine whether the fraction holiday firms account for is exceptionally low during

12One might be concerned about the use of firm names. They might not be unambiguous and a few of them clearly have

multiple meanings. However, this seems unlikely to drive our main results. First, we study differences between two large

samples with several hundred firm names. Thus, any potential inaccuracies and inconsistencies are likely to cancel out.

Second, we are interested in shocks of search frequencies, i.e. we control for the expected level of queries. Third,“Google

Insights for Search” additionally provides a top search list with the terms most closely related to the original search. In an

attempt to manually cleanse the data, we used that information to exclude those firms that seemed most likely to distort

the analysis. Inferences remained unchanged. The alternative of relying on security identification numbers instead of firm

names turned out to be unproductive as search frequencies tend to be much lower, resulting in many missing values.

17

the holiday. This is what a news-based explanation of our findings would arguably predict.

To test this hypothesis, we match our sample with the I/B/E/S analyst buy/hold/sell-

recommendations database. This results in a total of 51,497 stock recommendations of

196 brokers, which cover more than 80% of the sample firms. For the eleven day pe-

riod centered around the holiday (t-5 to t+5), we then determine which fraction of all

recommendations issued on this day is attributable to holiday firms. The length of this

benchmark period is meant to account for the seasonality in earnings reports, but the

qualitative nature of our findings is robust to alternative control windows. We average

values for t. Values for the benchmark period (excluding t) are pooled to give rise to an

empirical benchmark distribution of relative analyst coverage for holiday firms. Relying

on the percentiles of this distribution, we are able to detect whether analyst informa-

tion transmission for holiday firms exhibits a negative shock. We distinguish between a

value-weighted analysis, in which multiple recommendations made for the same firm on

the same day are considered as multiple observations, and an equal-weighted analysis, in

which we regard such a scenario as a single observation. The latter tends to give more

weight to small firms, which less often receive several recommendations at the same day.

As a sensitivity check, we repeat the analysis now focusing on the review date, i.e. the

most recent date that an estimate is confirmed by an analyst to I/B/E/S as accurate.

Panel C of table 4 shows the fraction of total analyst coverage on the event day. Percentiles

are given in parentheses. A higher percentile indicates that holiday firm recommendations

account for a larger fraction of the total number of recommendations issued. In all speci-

fications, coverage does not seem to decrease for firms located in holiday regions. Judging

from the percentiles of the distribution, the holiday rather appears like an average day

of the benchmark period.13 Moreover, the value-weighted and the equal-weighted analysis

show a similar picture, suggesting there are no marked differences between large and small

firms.

As a final test, we study shocks in media coverage in three leading German daily business

newspapers, which are published nation-wide.14 The comprehensive database, for which

13One might be concerned about noise in the data. Indeed, a similar bootstrapping approach as outlined in footnote 11

reveals that the dispersion of simulated outcomes is quite substantial. However, even the lower bound of the 99% confidence

interval does not touch the 10th percentile of the benchmark distribution, which contradicts an information-based story.

14IVW, a German auditing institution that provides data on the distribution of media products, reports that Sueddeutsche

Zeitung had the second highest circulation among nationwide published daily papers over the period 2000 to 2008. It ranks

18

panel A of table 5 gives more details, is based on daily data from January 1, 2000 on

and comprises Financial Times Deutschland, Handelsblatt and Sueddeutsche Zeitung.

Searching factiva and genios, articles about each firm for each day and in each paper are

manually collected.15 This results in a total of 126,125 news stories covering almost 94% of

our sample firms. Again, we distinguish between a value-weighted and an equal-weighted

analysis. The latter relies on a dummy variable that simply states whether a firm has

received press coverage on a given day. The former individually counts each article. It

thus takes on values greater than one if there are several firm news stories in the same

paper, or if several papers cover the firm. In doing so, it tends to give more weight to

blue chips and big news. For further insights, we additionally split firms into large and

small stocks, as before. Across all specifications, there is considerable variation in daily

media coverage. For instance, on a given day, the fraction of news stories attributable

to firms that didn’t make the news the day before, is 63% (52%) for the equal-weighted

(value-weighted) analysis on average. Focussing on small firms yields even 91% (90%).

Panel B shows results from a test similar to the one used for analyst coverage. We analyze

whether aggregated media coverage for holiday firms is abnormally low around the holiday.

We consider both the event day and the following day, as information becoming public

at t can not be published by newspapers before t+1. To assess statistical significance, we

calculate the percentage of total media coverage attributable to holiday firms for each day

of the year.16 We then analyze the fraction of press coverage around the holiday relative

to the whole empirical distribution, which does not exhibit strong seasonal patterns. The

analysis produces mixed results. Around All Saints’ Day, media coverage for holiday firms

first if one excludes the popular press. Among the daily newspapers with a strong focus on business and economics,

Handelsblatt and Financial Times Deutschland rank first and second. In the fourth quarter of 2008, the three newspapers

had a combined circulation of more than 800,000 copies per day.

15Similarly as in Tetlock et al. (2008), we thereby require the article to mention at least twice the name or security

identification number of the firm. This procedure aims at reducing noise and identifying relevant firm-specific articles.

Coverage for Financial Times Deutschland starts on January 1, 2001.

16We thereby account for the fact that not all newspapers are published at each day of the year: At Corpus Christi,

Handelsblatt and Sueddeutsche Zeitung are not distributed. At Epiphany, Sueddeutsche Zeitung is not published. This is

unlikely to materially influence our analysis. First, for the more important date t+1, all newspapers are available. Findings

are similar as on date t. Second, the results from the equally- and from the value-weighted analysis are similar in general.

This suggests that relevant information is, for the most part, picked up by each of these leading newspapers so that partly

relying on a subset of them does not change the qualitative nature of the results. This line of reasoning is also supported

by the highly significant correlations in daily firm-level media coverage as shown in panel A of table 5.

19

is indeed significantly lower, which, in line with findings from the test on corporate news

releases, appears to be driven by larger firms. However, there is no similar evidence for any

of the other holidays. In fact, press coverage is sometimes even higher than on average.

In the overall picture, results point against a strong general drop in media exposure for

both large and small holiday firms. To gain more insight, we implement a more formal

regression approach. We create the dummy Newsi,t which indicates for each firm i on

each day of the eleven trading days period centered around the regional holiday whether

a news article was published.17 We then pool the observations and run the following probit

regression separately for Epiphany, All Saints’ Day and Corpus Christi:

NEWSi,t = β0 + β1EventDummy + β2HolidayRegionDummy + β3InteractionTerm+ Y earDummies+ ϵi,t

(3)

The event dummy indicates the holiday within the event period. We also run analogous

regressions for the days preceding and following the holiday. Of interest is the interaction

effect between the event dummy and the holiday region dummy. If the volume shock was a

result of systematic cross-sectional differences in press coverage, then it should consistently

attain a significantly negative sign. Panel C of table 5 reports results from the nine probit

regressions. Magnitude and significance of the interaction effect are assessed as suggested

by Ai and Norton (2003). Again, the only significant results are found for the All Saints’

Day sample. Thus, the findings at best sporadically point to differences in information

release picked up by the press.

We finally incorporate additional control variables in our pooled regression approach as

outlined in section 4.2. For data availability reasons, we focus on the period from 2000

to 2009, and add a set of dummies to control for the effect of media coverage and ad

hoc disclosures on any day between t-5 and t+5. Panel D of table 5 reveals that the

regional holiday effect keeps its significance, both from an statistical and an economic

point of view. Modifying the analysis by focussing only on those stocks for which we have

additional information about the release of other corporate news yields similar results.

Taken together, the combined findings from all tests in this section provide the following

17We choose this binary approach to reduce the overcounting of news about the same subject from multiple sources.

However, an analysis focussing on the actual number of news produces very similar results. The eleven day period is largely

representative for the media coverage in the whole year.

20

picture: First, we cannot dismiss the hypothesis of lower information intensity for holiday

firms as there is minor evidence of differences in news release. Their lack of robustness

and small magnitude, however, suggest they are unlikely to fully explain the economically

substantial and pervasive drop in trading volume for holiday firms. The evidence points

against persistent disparities between small and large firms. Second, controlling for po-

tential differences in news arrival to the extent possible, our results remain qualitatively

unchanged. Third, these findings strongly confirm hypothesis 1.

5. Determinants of the Regional Holiday Effect

Firm characteristics What factors drive the cross-sectional heterogeneity in negative

turnover shocks? To answer this question, we first construct a firm-specific measure of ab-

normal turnover, defined as actual (logarithmized) turnover during the holiday minus the

average turnover during t-20 to t-2. For robustness reasons, we then run pooled regressions

separately for each of the three holiday samples as well as for two sample periods.

Hypothesis 2, inspired by the model of Merton (1987), posits that the turnover shock

should be particularly strong if a firm is visible primarily to local investors. Merton argues

that investor recognition is a function of the shadow cost of information, which, in his

model, depends on idiosyncratic risk, relative market size and the completeness of the

shareholder base. We thus use the logarithm of a firm’s market capitalization, as measured

at the end of the preceding year, and a firm’s idiosyncratic risk as independent variables.

Idiosyncratic risk is defined as the standard deviation of the residual obtained by fitting

a Carhart (1997) four factor model (as described in section 4.3) to the daily return time-

series from t-180 to t-6.

Market capitalization is strongly negatively related to the total number of shareholders

(e.g. Grullon et al. (2004)) and positively related to the fraction of local investors (e.g. Zhu

(2003)). Consequently, we expect a smaller drop in volume for larger firms, which implies

a positive coefficient for firm size. Idiosyncratic risk, on the other hand, increases the

shadow cost of information. Local investors are commonly thought to possess (actual or

perceived) informational advantages. Thus, local clienteles should account for a relatively

large proportion in the trading of stocks with high idiosyncratic risk, which should go along

21

with a more pronounced negative volume shock during regional holidays. Consequently,

a negative coefficient is expected.

In addition, we employ with residual media coverage a third proxy, which is orthogonal

to size and available for the years 2001 to 2009. The residual is obtained from yearly

cross-sectional regressions of the number of firm-specific press articles in the previous

year on its lagged average market size, turnover and absolute return as well as on a set

of control variables for industry and DAX30 membership. Press articles are taken from

the comprehensive media coverage database described in section 4.3. Residual coverage is

designed to proxy for the unexpected high or low weight the media attaches to a certain

firm. Given the importance of leading business newspapers in disseminating information

to a broad audience, residual media coverage is an intuitive measure of firm visibility.

Consequently, we expect a positive coefficient.

Previous research and our baseline analysis highlighted the importance of current returns

for current turnover. We thus include the same two return-based variables in the regres-

sion. To control for additional effects induced by medium-term return continuation, we

consider the loading on the momentum factor (WML), obtained from a regression of

stock returns on the Carhart (1997) four factor model. The loadings on the market as

well as value factor (RMRF , HML) are considered as proxies for systematic risk (e.g.

Chordia et al. (2007)). The intercept from this regression (Alpha) is included as it has

been argued to contain a premium related to liquidity or heterogeneous information (e.g.

Lo and Wang (2000)). Moreover, we include a rural dummy for firms located outside a

metropolitan region. The “only game in town effect” (Hong et al. (2008)) suggests a neg-

ative coefficient. Inspired by e.g. Seasholes and Wu (2007), a 52 week high dummy for

stocks whose price has exceeded this bound in the previous week is considered. Finally,

we include a set of industry dummies.

For each holiday, table 6 displays univariate and multivariate results for the whole sample

period. We report coefficients for the subperiod 2001 to 2009 separately. These coefficients

additionally include residual media coverage and controls for the availability of press

articles as well as ad hoc disclosures around the event date (see also section 4.3).


22

The findings are broadly consistent with our expectations. Investor recognition seems an

important driver of the turnover shock. All proxies consistently attain the predicted sign

and, with the exception of idiosyncratic risk, are persistently statistically significant. The

effect of market capitalization is clearly the strongest, but residual media coverage has

an incremental effect. The magnitude of the results is also of economic importance: As a

rough estimate, for example, a one standard deviation change in firm size has a similar

impact as a one standard deviation change in stock return. The current absolute return

is highly significant. The dummies for rural firms and the 52 week high attain coefficients

as predicted, but their importance is not robust. The other controls seem to play only a

minor role. In sum, hypothesis 2 can broadly be confirmed. The regional holiday effect is

considerably stronger for firms less visible to non-local investors.

Investor characteristics In this section, we aim at gaining additional insights from the

daily tracking records of roughly 3,000 retail clients of a German online broker from Jan-

uary 1997 to April 2001. Comprehensive information about the sample, such as details

about the construction of portfolio holdings, is given in Glaser and Weber (2009) and

Glaser and Weber (2007). Sample investors account for a total of 316,134 stock trans-

actions, out of which 136,125 take place in 965 German firms. As the latter represent

roughly 50% of all transactions traceable via Datastream, investors seem to exhibit a

strong home bias. Panels A to C of table 7 provide descriptive statistics, which show that

sample investors trade frequently. The mean (median) number of transactions in German

firms is 47 (22), leading to a total sample trading volume of more than 750 million Euro.


For the purpose of our analysis, the data set has two advantages. First, the broker does

not offer investment advice. Therefore, trading decisions are not affected by bank recom-

mendations. Second, online broker investor trading on regional holidays is not restricted

in any way. Results suggestive of localized trading might thus be considered conservative

in the sense that other investors might face higher obstacles, such as finding an open bank

office.18 A disadvantage of the sample is that investor location is not provided. Given this

18Note again that this does not have cross-sectional implications for abnormal stock turnover unless local investors’

trading decisions systematically deviate from remote investors’ buys and sells.

23

limitation, exploring to what extent investors exhibit local bias (in addition to home bias),

is not a straightforward exercise. We thus start our analysis with the reasonable assump-

tion that a disproportionate fraction of the broker’s clients live in the region in which the

broker is headquartered. Locally biased investors should then have a strong preference for

firms also located in the affected metropolitan area.19 To test this, we compute a sample

investor preference measure as the difference between a firm’s brokerage weight and its

weight in the market portfolio of German stocks. The firm’s brokerage weight is defined

as the total volume invested in the firm’s stock by the broker’s clients divided by the total

volume the clients invest in all German stocks at the time. We do so at the beginning of

each month and for each firm traded at least once on any day by any sample investor. We

average stock-specific time-series to obtain an average estimate, based on which we sort

firms in one of three portfolios of equal size: “Low preference”, “medium preference” and

“high preference”. Then, for each of these portfolios, we determine the fraction of firms

located in the same metropolitan area as the online broker itself. As the metropolitan area

turns out to be large, there is sufficient level of diversification. Consequently, if sample

investors were not locally biased, we would expect the fraction of firms located near the

online broker to be similar across preference portfolios. However, panel D shows that this

is not what we find. The fraction of the “medium preference” portfolio is standardized

to 1. Therefore, the value 1.29 for the “high preference” portfolio implies that there are

close to 30% more local firms than would be expected on average by chance.

Having verified the existence of at least some local bias, we turn to a test suggested by

hypothesis 1: Holiday trading activity should decrease in local bias. We label each firm

located in the broker’s metropolitan area a “low preference”, “medium preference” or

“high preference” firm. Then, we compute the daily fraction of aggregate sample investor

trading volume that is attributable to each of these portfolios, leading to an empirical

benchmark distribution for portfolio-specific relative trading activity. Similarly as in pre-

vious tests, we determine the percentile of the distribution that is observable during the

day of the regional holiday.20 Hypothesis 1 predicts that these percentiles should decrease

19To sharpen the analysis, we focus on the metropolitan area classification as outlined in section 4.2. Results are similar

when we make use of states instead. Moreover, to mitigate the effect of a few extremely large trades that could materially

affect the analysis, we winsorize investor transactions at the 99.9% level in all following tests.

20To sharpen the analysis, we focus on the holiday that most clearly separates the broker’s metropolitan area as a holiday

region from as many other metropolitan areas as possible.

24

in local investors’ preference - firms with a high degree of local bias should exhibit a more

pronounced shock in relative trading volume. Panel E shows that this is indeed what

we find. The “high preference portfolio” temporarily exhibits the lowest trading activity,

no matter if one focuses on the total Euro volume traded, the number of transactions

conducted or the number of investors trading.

We now turn to hypothesis 3, which posits that the turnover drop in small stocks is dis-

proportionately caused by private investors. To this end, we aggregate data and conduct

tests based on shocks in a measure called Ratioi,t. For holiday i, it is computed as the

overall fraction of daily “holiday firm trading” by online broker investors divided by the

fraction of daily “holiday firm trading” by the whole market. The rationale is as follows:

As the daily trading volume of the investor sample is positively correlated (0.39) with the

daily market trading volume for these firms, it appears justified to use market volume

as a benchmark. By focussing on shocks of Ratioi,t, one mitigates the problem of lacking

information on investor location, as the expected level of trading in each group of stocks

is automatically accounted for. To identify shocks, we control for the autoregressive prop-

erties of Ratioi,t by employing AR(p)-processes similar to Connolly and Stivers (2003).

Shocks are defined as the residual ϵi,t from the following regression:

Ratioi,t = βi,0 +p∑

k=1

βi,kRatioi,t−k + ϵi,t (4)

P denotes the maximum lag, up to which each estimated coefficient on each lagged term

of Ratioi,t is individually significant, and takes on values between two and five for the

specifications described below. ϵi,t can thus be interpreted as unexpected daily changes in

holiday firm trading of retail investors as compared to the whole market.

To test hypothesis 3, we compute Ratioi,t separately for the whole sample as well as for

small and large stocks, split by the median of market capitalization at the beginning

of the year. We do this for each of the three holidays. We then determine the most

suitable AR(p)-process for each of the nine specifications and run the regression as given

in Equation (4). This results in nine shock time series. Finally, we apply these to the seven

holiday observations that take place on a trading day during our retail investor sample

period: Epiphany is celebrated four times, All Saints’ Day twice and Corpus Christi once.

Panel F of table 7 reports the percentiles of the shock variables for each stock sample (all,

25

large, small). The results for large stocks and for the whole sample appear like random

draws from the distribution. In other words, there are no systematic differences between

individual investors and the overall market. However, focussing explicitly on smaller firms,

a clear pattern emerges: Online broker investors’ trading activity consistently exhibits

negative shocks at the day of the holiday when benchmarked against the whole market.

The value of the shock variable is well below its median for every single observation.

Assuming independence, the likelihood of observing this result by chance is below 1%. In

other words, the findings are consistent with hypothesis 3.

6. Conclusion

We run a series of natural experiments which collectively suggest that local bias leaves

discernible traces in the cross-section of firm-level trading activity. The German setting

allows us to compare abnormal turnover in several treatment groups, i.e. hundreds of

firms in holiday regions, with turnover in control groups, i.e. in many ways very similar

firms in non-holiday regions. Ceteris paribus, firms in holiday regions are remarkably

less traded. This finding is mostly confined to the day of the holiday itself, statistically

significant, economically meaningful, robust, and does not appear to be completely driven

by differences in information release. Instead, consistent with a local bias explanation and

the model of Merton (1987), it is particularly strong for firms less recognized by non-local

investors. Moreover, in line with predictions of previous research, the turnover shock in

smaller stocks seems to be disproportionately caused by individual investors.

The basic message of this study is a simple one: Local investor clienteles are strong and

pervasive enough to generate frictions segmenting the stock market along a geographical

line. Our analysis also contributes to research on determinants of firm-level trading volume

by establishing cross-sectional regularities related to firm location, firm visibility, and

investor clienteles. Moreover, by uncovering a link between the potentially powerful role

of local investors, investor distraction, and the cross-section of firm turnover, we might

provide a new fruitful starting point for the emerging research on the joint dynamics of

investor attention, trading volume, and price discovery.

26

References

Ai, C., and E. C. Norton, 2003, “Interaction terms in logit and probit models,” Economic

Letters, 80, 123–129.

Baik, B., J.-K. Kang, and J.-M. Kim, 2010, “Local institutional investors, information

asymmetries, and equity returns,” Journal of Financial Economics, 97, 81–106.

Bailey, W., A. Kumar, and D. Ng, 2008, “Foreign investments of U.S. individual investors:

Causes and consequences,” Management Science, 54, 443–459.

Barberis, N., A. Shleifer, and J. Wurgler, 2005, “Comovement,” Journal of Financial

Economics, 75, 283–317.

Bodnaruk, A., 2009, “Proximity always matters: Local bias when the set of local companies

changes,” Review of Finance, 13, 629–656.

Boersch-Supan, A., M. Coppola, L. Essig, A. Eymann, and D. Schunk, 2009, “The German

SAVE study - Design and results,” MEA Study No. 6.

Brown, J. R., Z. Ivkovic, P. A. Smith, and S. Weisbenner, 2008, “Neighbors matter: Causal

community effects and stock market participation,” Journal of Finance, 63, 1509–1531.

Carhart, M. M., 1997, “On persistence in mutual fund performance,” Journal of Finance,

52, 57–82.

Chae, J., 2005, “Trading volume, information asymmetry, and timing information,” Jour-

nal of Finance, 60, 413–442.

Chordia, T., S.-W. Huh, and A. Subrahmanyam, 2007, “The cross-section of expected

trading activity,” Review of Financial Studies, 20, 709–740.

Connolly, R., and C. Stivers, 2003, “Momentum and reversals in equity-index returns

during periods of abnormal turnover and return dispersion,” Journal of Finance, 58,

1521–1556.

Coval, J. D., and T. J. Moskowitz, 1999, “Home bias at home: Local equity preference in

domestic portfolios,” Journal of Finance, 54, 2045–2073.

, 2001, “The geography of investment: Informed trading and asset prices,” Journal

of Political Economy, 109, 811–841.

27

Da, Z., J. Engelberg, and P. Gao, 2011, “In search of attention,” forthcoming Journal of

Finance.

DellaVigna, S., and J. M. Pollet, 2009, “Investor inattention and Friday earnings an-

nouncements,” Journal of Finance, 64, 709–749.

Dorn, D., and G. Huberman, 2005, “Talk and action: What individual investors say and

what they do,” Review of Finance, 9, 437–481.

Dorn, D., G. Huberman, and P. Sengmueller, 2008, “Correlated trading and returns,”

Journal of Finance, 63, 885–920.

Fama, E. F., and K. R. French, 1993, “Common risk factors in the returns on stocks and

bonds,” Journal of Financial Economics, 33, 3–56.

Feng, L., and M. S. Seasholes, 2004, “Correlated trading and location,” Journal of Fi-

nance, 59, 2117–2144.

Frieder, L., and A. Subrahmanyam, 2004, “On non-secular regularities in trading activity

and stock returns,” Financial Analysts Journal, 60, 29–34.

Gao, X., and T.-C. Lin, 2010, “Do behavioral needs influence the trading activity of in-

dividual investors? Evidence from repeated natural experiments,” Unpublished working

paper, University of Hong Kong.

Glaser, M., and M. Weber, 2007, “Overconfidence and trading volume,” Geneva Risk and

Insurance Review, 32, 1–36.

, 2009, “Which past returns affect trading volume?,” Journal of Financial Markets,

12, 1–31.

Goetzmann, W. N., and A. Kumar, 2008, “Equity portfolio diversification,” Review of

Finance, 12, 433–463.

Grinblatt, M., and M. Keloharju, 2001, “How distance, language, and culture influence

stockholdings and trades,” Journal of Finance, 56, 1053–1073.

Grullon, G., G. Kanatas, and J. P. Weston, 2004, “Advertising, breadth of ownership,

and liquidity,” Review of Financial Studies, 17, 439–461.

28

Hau, H., 2001, “Location matters: An examination of trading profits,” Journal of Finance,

56, 1959–1983.

Hirshleifer, D., S. S. Lim, and S. H. Teoh, 2009, “Driven to distraction: Extraneous events

and underreaction to earnings news,” Journal of Finance, 64, 2289–2325.

Hong, H., J. D. Kubik, and J. C. Stein, 2004, “Social interaction and stock-market par-

ticipation,” Journal of Finance, 59, 137–163.

, 2005, “Thy neighbor’s portfolio: Word-of-mouth effects in the holdings and trades

of money managers,” Journal of Finance, 60, 2801–2824.

, 2008, “The only game in town: Stock-price consequences of local bias,” Journal

of Financial Economics, 90, 20–37.

Hong, H., and J. Yu, 2009, “Gone fishin’: Seasonality in trading activity and asset prices,”

Journal of Financial Markets, 12, 672–702.

Hou, K., L. Peng, and W. Xiong, 2009, “A tale of two anomalies: The implications of

investor attention for price and earnings momentum,” Unpublished working paper, Ohio

State University, City University of New York, Princeton University.

Huberman, G., 2001, “Familiarity breeds investment,” Review of Financial Studies, 14,

659–680.

Ince, O. S., and R. B. Porter, 2006, “Individual equity return data from Thomson Datas-

tream: Handle with care!,” Journal of Financial Research, 29, 463–479.

Ivkovic, Z., and S. Weisbenner, 2005, “Local does as local is: Information content of the

geography of individual investors’ common stock investments,” Journal of Finance, 60,

267–306.

, 2007, “Information diffusion effects in individual investors’ common stock pur-

chases: Covet thy neighbors’ investment choices,” Review of Financial Studies, 20, 1327–

1357.

Korniotis, G. M., and A. Kumar, 2010, “State-level business cycles and local return pre-

dictability,” Unpublished working paper, University of Miami.

Kumar, A., and C. M. Lee, 2006, “Retail investor sentiment and return comovements,”


29

Lo, A. W., and J. Wang, 2000, “Trading volume: Definitions, data analysis, and implica-

tions of portfolio theory,” Review of Financial Studies, 13, 257–300.

Loughran, T., and P. Schultz, 2004, “Weather, stock returns, and the impact of localized

trading behavior,” Journal of Financial and Quantitative Analysis, 39, 343–364.

, 2005, “Liquidity: Urban versus rural firms,” Journal of Financial Economics, 78,

341–374.

Lyon, J. D., B. M. Barber, and C.-L. Tsai, 1999, “Improved methods for tests of long-run

abnormal stock returns,” Journal of Finance, 54, 165–201.

Massa, M., and A. Simonov, 2006, “Hedging, familiarity and portfolio choice,” Review of

Financial Studies, 19, 633–685.

Merton, R. C., 1987, “A simple model of capital market equilibrium with incomplete

information,” Journal of Finance, 42, 483–510.

Newey, W. K., and K. D. West, 1987, “A simple, positive semi-definite, heteroskedasticity

and autocorrelation consistent covariance matrix,” Econometrica, 55, 703–708.

Petersen, M. A., 2009, “Estimating standard errors in finance panel data sets: Comparing

approaches,” Review of Financial Studies, 22, 435–480.

Pirinsky, C., and Q. Wang, 2006, “Does corporate headquarters location matter for stock

returns?,” Journal of Finance, 61, 1991–2015.

Seasholes, M. S., and G. Wu, 2007, “Predictable behavior, profits, and attention,” Journal

of Empirical Finance, 14, 590–610.

Seasholes, M. S., and N. Zhu, 2010, “Individual investors and local bias,” Journal of

Finance, 65, 1987–2011.

Shive, S. A., 2010, “An epidemic model of investor behavior,” Journal of Quantitative

and Financial Analysis, 45, 169–198.

, 2011, “Local investors, price discovery, and market efficiency,” Unpublished work-

ing Paper, University of Notre Dame.

Statman, M., S. Thorley, and K. Vorkink, 2006, “Investor overconfidence and trading

volume,” Review of Financial Studies, 19, 1531–1565.

30

Tetlock, P. C., M. Saar-Tsechansky, and S. Macskassy, 2008, “More than words: Quanti-

fying language to measure firms’ fundamentals,” Journal of Finance, 63, 1437–1467.

Tkac, P. A., 1999, “A trading volume benchmark: Theory and evidence,” Journal of

Financial and Quantitative Analysis, 34, 89–114.

Womack, K. L., 1996, “Do brokerage analysts’ recommendations have investment value?,”


Zhu, N., 2003, “The local bias of individual investors,” Unpublished working paper, Yale

University.

31

Figure 1: Geographic Distribution of Firm Headquarters and of Regional Holidays across Germany

This figure shows the location of firm headquarters across Germany. Headquarters are represented by black

circles; additional clusters of headquarters (with more than 20 firms) in a given city are represented by red

numbers. These correspond, from west to east, to the cities of Duesseldorf (28 firms), Cologne (36 firms),

Frankfurt (40 firms), Stuttgart (21 firms), Hamburg (57 firms), Munich (70 firms) and Berlin (48 firms).

Moreover, the figure exemplarily illustrates the geographic distribution of regional holidays across Germany.

Shown is the example of Epiphany, which is legally recognized only in the grey-shaded states of Baden-

Wuerttemberg (118 firms), Bavaria (180 firms), and Saxony-Anhalt (3 firms).

48

57

21

70

40

36

28

32

Tab

le1:

DescriptiveStatisticsof

EventStudySam

ples

This

table

provides

summary

statisticsofev

entstudysamplesforEpiphany(P

anel

A)andAllSaints’Day(P

anel

B).

Holiday(N

on−

Holiday)den

otesth

esample

of

firm

swhose

hea

dquarter

is(n

ot)

loca

tedin

aregionwhereth

eresp

ectiveholidayis

legallyreco

gnized

.M

edianmaca

pandmed

iandailyreturnreferto

thecross-sectional

med

ianoftimeseries

averages

ofsample

firm

s’market

capitaliza

tion(inmillionEuro)anddailyretu

rns,

resp

ectively.

IndustryConcentration

den

otesth

eHerfindahl

index

,co

mputedasth

esu

mofsquaredindustry

groupweights

(Datastream

Lev

el2industry

classifica

tion,10groups).Twoversionsare

shown.In

theeq

ual-weighted

version(E

W),

industry

weights

are

determined

byth

epercentageofsample

industry

groupfirm

s.In

thevalue-weightedversion(V

W),

industry

weights

are

determined

byth

erelativemarket

capitaliza

tionofindustry

groupobservations,

thereb

yex

cludingDAX30firm

s.Table

2ofth

eappen

dix

provides

more

detailed

inform

ationabout

industry

concentrationandindustry

composition.DAX

(DAX/M

DAX)refers

toth

efractionofsample

observationsattributable

toDAX30(D

AX30andMDAX)firm

s

inth

eperiod1988-2009(1994-2009).

TheMDAX

comprises50largeGermanco

mpaniesfrom

traditionalsectors

rankingim

med

iately

after

the30DAX

firm

s.

Panel

A:EpiphanySample

Region

Number

offirm

sMed

ianMacap

Dailymed

ianreturn

Industry

ConcentrationEW

(VW

)DAX

(DAX

+MDAX)

Holiday

301

1,037

-0.01%

0.15(0.16)

6.1%

(14.2%)

Non-H

oliday

491

977

-0.01%

0.15(0.16)

5.0%

(15.0%)

Tim

e-series

properties

ofequallyweightedturnov

erindices

basedondailydata

Region

Mean

SD

5th

Percentile

95th

Percentile

Correlation(H

oliday,Non-H

oliday

)

Holiday

0.21%

0.13%

0.06%

0.45%

0.86

Non-H

oliday

0.20%

0.12%

0.07%

0.43%

Panel

B:AllSaints’Day

sample

Region

Number

offirm

sMed

ianMacap

Dailymed

ianreturn

Industry

ConcentrationEW

(VW

)DAX

(DAX

+MDAX)

Holiday

509

1,155

-0.01%

0.15(0.17)

5.9%

(14.9%)

Non-H

oliday

283

704

-0.02%

0.16(0.18)

4.3%

(14.2%)

Tim

e-series

properties

ofequallyweightedturnov

erindices

basedondailydata

Region

Mean

SD

5th

Percentile

95th

Percentile

Correlation(H

oliday,Non-H

oliday

)

Holiday

0.20%

0.12%

0.06%

0.44%

0.83

Non-H

oliday

0.21%

0.13%

0.07%

0.46%

33

Tab

le2:

Firm

Turnover

AroundEpiphan

yan

dAllSaints’Day

:YearlyFama-MacB

ethRegressions

This

table

summarizesresu

ltsfrom

variousregressions.

Thedep

enden

tvariable

isth

enatu

rallogarith

moffirm

turn

over

onth

edayof(t),

priorto

(t−

1)orfollowing

(t+1)th

eholiday.

InPanel

AandC,ex

pectedturnover

ismea

suredasth

eaveragefirm

turn

over

over

t-20to

t-2.In

Panel

BandD,amarket

model

oftu

rnover

calibrated

from

t-60to

t-2is

employed

instea

d.Ret

+,t

(Ret

+,t−1)den

ote

thestock

retu

rnatt(t-1)if

positiveand

zero

oth

erwise.

Ret

−,t

(Ret

−,t−1)are

defi

ned

analogously.

T-statistics(inparenth

eses)are

computedwithNew

ey/W

est(1987)-adjusted

standard

errors.Statisticalsignifica

nce

atth

eten,fiveandone-percentlevel

isindicatedby

*,**,and***,resp

ectively.

Thereported

R2is

calculatedasth

eaverageofadjusted

R2.Pred

ictedsignden

otesth

efractionofaneg

ativeholidaydummyco

efficien

tatt.

PanelA:Firm

SpecificExpected

Turn

over(N

=5,657)Aro

und

Epiphany

DependentVariable

Constant

Expected

Turn

over

Ret +

,tRet −

,tRet +

,t−

1Ret −

,t−

1Reg

ionalholiday

dummy

R2

Firm

turn

overatt-1

-1.49***(-6.13)

0.94***(3

3.99)

11.00***(8

.49)

8.97**(2

.71)

3.69(1

.25)

-1.65(-0.42)

-0.05

(-1.21)

0.71

Firm

turn

overatt

-1.91***(-6.77)

0.91***(2

6.66)

19.25***(4

.28)

18.90***(3

.24)

4.63*(1

.84)

-3.45(-1.53)

-0.23***

(-5.59)

0.69

Firm

turn

overatt+

1-1.50***(-6.32)

0.94***(3

7.97)

16.54***(6

.09)

13.86***(6

.01)

5.38*(2

.02)

4.70(1

.34)

-0.10**

(-2.40)

0.71

Predicted

sign

14/14

95%

conf.

interval

-0.32/-0.14

PanelB:M

ark

etM

odelExpected

Turn

over(N

=5,657)Aro

und

Epiphany

DependentVariable

Constant

Expected

Turn

over

Ret +

,tRet −

,tRet +

,t−

1Ret −

,t−

1Reg

ionalholiday

dummy

R2

Firm

turn

overatt-1

-0.64***(-4.02)

0.96***(8

3.87)

12.46***(9

.07)

11.64***(3

.52)

1.74(0

.82)

1.54(0

.42)

-0.00

(-0.15)

0.79

Firm

turn

overatt

-0.97***(-5.60)

0.94***(7

0.28)

19.41***(4

.85)

16.77***(3

.14)

5.20**(2

.63)

-0.83(-0.72)

-0.18***

(-9.33)

0.79

Firm

turn

overatt+

1-0.58***(-3.82)

0.96***(8

6.28)

16.59***(6

.18)

14.46***(5

.78)

5.86***(3

.12)

3.09(0

.97)

-0.05*

(-2.11)

0.80

Predicted

sign

14/14

95%

conf.

interval

-0.22/-0.14

PanelC:Firm

SpecificExpected

Volume(N

=6,485)Aro

und

All

Saints’Day

DependentVariable

Constant

Expected

Turn

over

Ret +

,tRet −

,tRet +

,t−

1Ret −

,t−

1HolidayReg

ion

Dummy

R2

Firm

turn

overatt-1

-1.44***(-8.84)

0.94***(4

9.02)

15.05***(5

.57)

13.13***(5

.01)

2.81(1

.46)

0.90(0

.40)

-0.00

(-0.11)

0.76

Firm

turn

overatt

-2.64***(-8.73)

0.85***(2

3.76)

22.21***(5

.36)

20.09***(5

.01)

5.63***(3

.85)

-3.87(-1.64)

-0.13***

(-4.03)

0.70

Firm

turn

overatt+

1-1.60***(-8.02)

0.92***(3

6.16)

13.74***(4

.88)

15.76***(4

.86)

8.98***(3

.04)

6.41***(4

.46)

-0.06*

(-1.80)

0.75

Predicted

sign

13/16

95%

conf.

interval

-0.20/-0.06

PanelD:M

ark

etM

odelExpected

Volume(N

=6,485)Aro

und

All

Saints’Day

DependentVariable

Constant

Expected

Turn

over

Ret +

,tRet −

,tRet +

,t−

1Ret −

,t−

1HolidayReg

ion

Dummy

R2

Firm

turn

overatt-1

-0.58***(-4.69)

0.96***(6

5.99)

14.20***(4

.33)

15.53***(4

.90)

6.21***(3

.42)

0.87(0

.40)

-0.04

(-0.76)

0.80

Firm

turn

overatt

-1.72***(-9.86)

0.88***(5

9.10)

22.48***(6

.10)

20.02***(5

.80)

5.11***(3

.89)

-1.11(-0.62)

-0.16***

(-4.87)

0.77

Firm

turn

overatt+

1-0.62***(-5.64)

0.96***(6

5.00)

14.19***(4

.01)

14.38***(5

.06)

6.80***(3

.43)

6.41***(3

.82)

-0.10**

(-2.64)

0.80

Predicted

sign

13/16

95%

conf.

interval

-0.23/-0.09

34

Table 3: Robustness Checks

This table displays the coefficient in front of the holiday dummy obtained from various regressions to test for

the robustness of our baseline results. T-statistics are reported in parentheses. Statistical significance at the

ten, five and one-percent level is indicated by *, **, and ***, respectively.

Epiphany All Saints’ Day

Panel A: Mean and Median Factor Loadings on the Regional Holiday Dummy from Placebo Treatments

Firm specific expected turnover Mean: -0.001, Median: -0.004 Mean: 0.005, Median: 0.001

Market model expected turnover Mean:0.007, Median: 0.009 Mean: 0.005, Median: 0.006

Panel B: Alternative Pre-event Periods

Firm specific expected turnover in t-10 to t-2 -0.24*** (-5.54) -0.13*** (-3.45)



Market model expected turnover in t-100 to t-2 -0.17*** (-7.20) -0.17*** (-3.70)



Panel C: Interacting Return Variables with Holiday Dummies

Firm specific expected turnover -0.29*** (-4.29) -0.17*** (-2.82)

Market model expected turnover -0.22*** (-5.69) -0.21*** (-3.54)

Panel D: Omitting Stocks With Zero Trading Volume on Event Day



Panel E: Using Ordinary Turnover

Firm specific expected turnover -0.02%*** (-3.80) -0.02%*** (-3.10)

Market model expected turnover -0.02%*** (-3.31) -0.02%*** (-3.82)

Panel F: Pooled Regression With Year Dummies and Standard Errors Clustered by Firm

Firm specific expected turnover -0.23*** (-4.73) -0.13** (-2.51)


Panel G: Analysis Based on Metropolitan Areas



Panel H: Regional Holiday Effects on Corpus Christi (Since 2000, Econometric Approach as in Panel F)

Firm specific expected turnover Market model expected turnover

-0.20*** (-2.67) -0.20*** (-3.61)

Panel I: Carnival Monday

Firm specific expected turnover Market model expected turnover

-0.05** (-2.38) -0.06** (-2.74)

35

Table 4: Tests for Cross-Sectional Differences in News Arrival

This table summarizes results from various tests aimed at detecting potential cross-sectional differences in news

arrival between holiday and non-holiday firms at the day of the holiday (=t). Large firms (Small firms) refer

to stocks with a market value larger (smaller) than the median stock, measured at the beginning of the year.

Statistical significance at the ten, five and one percent level is indicated by *, **, and ***, respectively. Panel

A reports differences in shocks in absolute abnormal returns. To this end, daily absolute abnormal returns for

each firm during t-5 to t+5, as obtained from a German version of the Carhart (1997) four factor model, are

computed. Factor loadings are estimated from time-series regressions from t-66 to t-6. For both the holiday

and the non-holiday sample, firm-specific shocks are computed as the absolute abnormal return at t minus

the average absolute abnormal return in t-5 to t+5 (excluding t). The table reports the difference between the

median shock value for the holiday sample and the median shock value for the non-holiday sample, averaged

across years. Statistical significance is assessed by bootstrapping as described in footnote 11. Panel B reports

the coefficient in front of the regional holiday dummy as obtained from pooled regressions of daily firm-specific

abnormal search volume in Google on dummies for regional holidays, years and industry groups. Abnormal

search volume is computed as the difference between the search volume at t and the average search volume

in t-10 to t-2, divided by the standard deviation of search volume in this pre-event period. T-statistics are

reported in parentheses. The last column reports p-values as obtained from an F-test of joint significance of

all three holiday dummies. Panel C shows the average fraction of total analyst recommendations and reviews

attributable to holiday firms at t. Only recommendations and reviews issued (not outstanding) on a given day

are considered. In a similar way, the fraction holiday firms account for is also computed for every other day in

t-5 to t+5. These values are pooled to construct an empirical benchmark distribution of analyst coverage in

a nearby period. Values in parentheses represent the percentiles of this distribution as achieved at t. A higher

percentile indicates that holiday firm recommendations account for a larger fraction of the total number of

recommendations. In the value-weighted (equal-weighted) analysis, multiple recommendations of the same

firm are considered as multiple observations (single observation).

Panel A: Differences in Shocks in Absolute Abnormal Returns

Dependent Variable Epiphany All Saints’ Day Corpus Christi Pooled

Difference in shock variable: All firms -0.05%* -0.02% -0.02% -0.03%

Difference in shock variable: Large firms -0.07%* -0.01% 0.01% -0.03%

Difference in shock variable: Small firms -0.02% -0.05% -0.03% -0.03%

Panel B: Abnormal Search Frequencies for Firm Names in Google

Dependent Variable Epiphany All Saints’ Day Corpus Christi P-value joint sign.

Shocks in online search queries: All firms -0.13 (-0.61) -0.15 (-1.40) -0.19 (-1.42) 0.17

Shocks in online search queries: Large firms -0.26 (-0.52) -0.33 (-1.02) -0.20 (-0.40) 0.19

Shocks in online search queries: Small firms -0.12 (-0.58) -0.16 (-1.55) -0.08 (-1.14) 0.16

Panel C: Fraction of Holiday Firm Analysts Recommendations

Dependent Variable Epiphany All Saints’ Day Corpus Christi

Value-weighted fraction of recommendations 40.34% (64) 68.57% (59) 81.51% (40)

Equally-weighted fraction of recommendations 40.37% (58) 69.90% (62) 82.57% (43)

Value-weighted fraction of reviews 41.67% (66) 63.64% (40) 87.87% (54)

Equally-weighted fraction of reviews 43.08% (72) 63.84% (44) 89.23% (54)

36

Table

5:Med

iaCoverag

ean

dtheRegional

Holiday

Effect

Panel

Bsh

owsth

eaveragefractionofnew

sstories

forholidayfirm

sont-1,t(theholiday)andt+

1.This

fractionis

alsoco

mputedforev

eryoth

erdayofth

eyea

r,giving

rise

toaben

chmark

distribution.Percentilesobtained

fort-1,tand

t+1are

reported

inparenth

eses.A

higher

valueindicatesth

atholidayfirm

saccountforalarger

fractionoftotalcoverage.

Largefirms(S

mallfirms)

referto

stock

swithamarket

valuelarger

(smaller)th

anth

emed

ianstock

.Panel

Creportsth

einteractioneff

ect,

computedasin

AiandNorton(2003),

obtained

from

probit

regressionsofpress

coverageonth

eregionalholidaydummy,

theev

entdate,holidaydummy*ev

entdate,and

yea

rdummies.

Panel

Dsh

owsth

eco

efficien

tsforth

eholidaydummy,

asobtained

byth

ebaselinepooledregression(see

section4.2)plusasetofdummiesfordailypress

coverageandadhocdisclosu

reswithin

t-5to

t+5.Statisticalsignifica

nce

atth

eten,fiveandonepercentlevel

isindicatedby*,**,and***,resp

ectively.

PanelA:DescriptiveSta

tistics(J

anuary

1,2000-January

15,2009)

Newsp

aper

Tota

lnumberofnewsstories

Covera

ge

PairwiseFirm-L

evelCorrelation

inM

edia

Covera

ge

Handelsblatt

61,956

90.34%

Handelsblatt

SZ

FTD

SZ

34,497

79.42%

Handelsblatt

10.23***

0.20***

FTD

29,672

73.44%

SZ

0.23***

10.19***

All

126,125

93.77%

FTD

0.20***

0.19***

1

PanelB:Fra

ction

ofNewsSto

riesaboutHolidayFirmsAro

und

theHoliday(=

t,Percentilesin

Parenth

eses)

Holiday

value-w

eighted

equal-weighted

tt+

1t

t+1

Epiphany:All

firm

s30.19%

(88)

22.91%

(14)

32.41%

(89)

27.05%

(53)

All

Saints’Day:All

firm

s55.83%

(3)*

*55.85%

(3)*

*59.33%

(4)*

*59.28%

(3)*

*

Corp

usChristi:

All

firm

s81.68(3

9)

82.63%

(46)

81.21(3

4)

81.32%

(34)

Epiphany:Larg

efirm

s30.02%

(89)

22.11%

(14)

31.55%

(87)

26.12%

(45)

All

Saints’Day:Larg

efirm

s55.33%

(4)*

*55.64%

(4)*

*58.56%

(3)*

*59.78%

(4)*

*

Corp

usChristi:

Larg

efirm

s82.35(3

7)

82.48%

(37)

82.01(3

5)

81.52%

(34)

Epiphany:Small

firm

s33.33%

(55)

29.41%

(44)

33.33%

(55)

29.41%

(44)

All

Saints’Day:Small

firm

s71.43%

(53)

57.14%

(20)

64.69%

(43)

57.14%

(20)

Corp

usChristi:

Small

firm

s66.67(3

8)

82.98%

(72)

66.67(3

8)

82.98%

(72)

PanelC:M

arg

inalEffects

ofPro

bit

RegressionsAro

und

theHoliday(=

t,z-valuesin

Parenth

eses)

Holiday

t-1

tt+

1

Epiphany

-0.41%

(-0.69)

0.70%

(1.16)

-0.03%

(-0.05)

All

Saints’Day

-0.01%

(-0.02)

-1.17%**(-2.15)

-1.16%*(-1.86)

Corp

usChristi

0.35%

(0.71)

-0.33%

(-0.60)

-0.80%

(-1.35)

PanelD:Pooled

Regression

(2000-2009)with

Controls

forM

edia

Covera

geand

Ad

HocDisclosu

res(t-sta

tisticsin

Parenth

eses)

Epiphany

All

Saints’Day

Corp

usChristi

Firm-specificexpected

turn

over

-0.20***(-3.86)

-0.11**(-1.98)

-0.16**(-2.16)

Mark

etmodelexpected

turn

over

-0.19***(-3.58)

-0.12**(-2.22)

-0.17**(-2.23)

37

Table

6:Determinan

tsof

theRegional

Holiday

Effect

This

table

summarizesth

emain

resu

ltsobtained

from

pooled

regressionsoffirm

-specificabnorm

altu

rnover

on

thedate

ofa

regionalholiday

(=t)

on

anumber

of

explainingvariables.

Only

firm

swhose

hea

dquarter

isloca

tedwithin

theregionwhereth

eholidayis

legallyreco

gnized

are

included

.Theindep

enden

tvariablesinclude

retu

rnco

ntrols

(see

table

2foradetailed

description);

thelogoflagged

firm

market

capitaliza

tion;idiosyncraticrisk;residualmed

iacoverage;

theloadingsonth

emarket,

valueandmomen

tum

factor(R

MRF,HML,W

ML,resp

ectively);

theintercep

tfrom

thatregression(A

lpha);

aru

raldummy;a52weekhighdummy;co

ntrols

formed

ia

coverageandadhocdisclosu

resaroundth

eev

ent;

asetof10industry

dummiesobtained

from

theDatastream

level

2industry

classifica

tion;yea

rdummies.

Standard

errors

are

clustered

byfirm

.Statisticalsignifica

nce

atth

eten,fiveandone-percentlevel

isindicatedby*,**,and***,resp

ectively.

PanelA:Determ

inants

ofth

eRegionalHolidayEffect:

Univariate

Resu

lts

Epiphany(1

988-2009)

Epiphany(2

001-2009)

All

Saints’Day(1

988-2009)

All

Saints’Day(2

001-2009)

Corp

usChristi(2

001-2009)

Mark

etCapitalization

0.25***(1

0.30)

0.30***(9

.96)

0.19***(1

0.93)

0.22***(1

1.99)

0.26***(1

4.29)

Idiosy

ncra

ticRisk

-4.23(-1.29)

-3.59(-0.86)

-9.74***(-3.65)

-9.06***(-3.09)

-12.97***(-3.79)

ResidualM

edia

Covera

ge

0.13**(2

.11)

0.07*(1

.85)

0.07*(1

.81)

PanelB:Determ

inants

ofth

eRegionalHolidayEffect:

Multivariate

Resu

lts

Epiphany(1

988-2009)

Epiphany(2

001-2009)

All

Saints’Day(1

988-2009)

All

Saints’Day(2

001-2009)

Corp

usChristi(2

001-2009)

Mark

etCapitalization

0.24***(8

.60)

0.30***(8

.88)

0.16***(8

.69)

0.20***(1

0.24)

0.23***(1

2.92)

Idiosy

ncra

ticRisk

-0.71(-0.20)

-0.56(-0.54)

-9.06**(-2.33)

-8.62***(-2.62)

-7.00**(-1.97)

ResidualM

edia

Covera

ge

0.10*(1

.88)

0.07*(1

.89)

0.05*(1

.78)

Ret +

,t15.96***(7

.11)

15.27***(5

.44)

12.13***(4

.15)

14.57***(8

.50)

19.08***(9

.75)

Ret −

,t17.84***(7

.02)

19.04***(5

.87)

16.11***(8

.07)

16.34***(8

.29)

17.05***(7

.34)

RM

RF

0.12*(1

.74)

0.16*(1

.78)

0.04(0

.92)

0.09(1

.63)

0.13**(2

.17)

HM

LBeta

-0.03(-0.57)

-0.07(-1.29)

0.03(0

.62)

0.05(1

.34)

-0.03(-0.59)

WM

LBeta

-0.04(-0.84)

-0.10(-1.64)

0.03*(1

.95)

0.02(0

.38)

-0.04(-1.17)

Alpha

-0.86(-0.00)

2.16(0

.09)

-2.89(-0.25)

-3.80(-0.27)

17.58(1

.13)

Rura

lDummy

-0.16(-0.95)

0.14(0

.84)

-0.21**(-2.21)

-0.22**(-2.14)

-0.12(-1.25)

52W

eekHigh

Dummy

0.07(0

.62)

0.15(1

.15)

0.20**(2

.22)

0.27**(2

.32)

0.20**(2

.09)

Ad

hocDisclosu

re(t)

0.28(0

.90)

0.80**(2

.47)

0.50*(1

.90)

Media

Covera

ge(t+1)

0.32(1

.46)

-0.11(-1.28)

0.09(0

.88)

Constant

-2.12***(-8.25)

-3.12***(-10.31)

-1.58***(-9.65)

-3.12***(-10.31)

-3.12***(-10.31)

Industry

and

YearDummies

yes

yes

yes

yes

yes

R2

0.18

0.26

0.11

0.16

0.17

N1,997

1,037

3,963

2,422

2,794

38

Tab

le7:

OnlineBroker

Sam

ple:DescriptiveStatistics,

LocalBias,

andTradingShocksduringRegionalHoliday

s

This

table

presents

resu

ltsfrom

theanalysisofretail

investortradingbased

on

dailydata.Thesample

period

startson

January

1,1997and

endson

April17,2001.

Allvalues

are

inEuro.PanelsA

toC

providedescriptivestatistics.

Panel

Dsh

owsth

estandard

ized

fractionoffirm

sth

atare

loca

tedin

thesamemetropolitanareaas

theonlinebroker

across

sample

investorpreference

portfolios.

Investorpreference

forea

chstock

inth

eonlinebroker

sample

isdefi

ned

asth

edifferen

cebetweenafirm

’s

brokerageweightand

itsweightin

themarket

portfolioofGerman

stock

s,averaged

across

month

lyobservations.

Each

portfolioco

ntainsan

equalnumber

ofstock

s.

Values

reported

inpanel

Dare

standard

ized

bydividingea

chfractionbyth

efractionobtained

forth

emed

ium

preference

portfolio.PanelsE

andF

provideresu

ltsfrom

twoseparate

testsofsh

ock

sin

tradingactivityatth

edayofth

eholiday,

asdescribed

indetailin

thetext.

PanelA:Aggregated

Data

(1,084tradingdays,

2,901investors,965Germ

an

stock

s)

No.Tra

nsa

ctions

Tota

lTra

nsa

ction

Value

No.Sells

Tota

lValueofSells

No.Buys

Tota

lValueofBuys

136,125

765,300,000

59,770

381,900,000

76,355

383,300,000

PanelB:Cro

ss-sectionalSta

tisticsforIn

dividualTra

nsa

ctions

Mean

Value

Median

Value

SD

1%

99%

Tra

nsa

ctions:

All

5,622

2,733

16,044

128

44,407

Tra

nsa

ctions:

Buys

5,021

2,523

14,676

125

38,059

Tra

nsa

ction:Sells

6,390

3,066

17,607

140

50,832

PanelC:Cro

ss-S

ectionalSta

tisticsforIn

dividualIn

vestors

Mean

Median

SD

1%

99%

Numberoftransa

ctions

47

22

95

1447

Numberoffirm

straded

15

11

16

185

Valueoftransa

ctions

263,792

65,743

993,767

580

3,288,099

PanelD:Sta

ndard

ized

Fra

ction

ofFirmsLocated

inth

eBro

kerM

etropolita

nAreabySample

InvestorPreferencePortfolios

Low

Preference

Medium

Preference

High

Preference

0.98

11.29

PanelE:PercentilesofRelativeTra

dingActivityduringth

eHolidaybyPreferencePortfolios

All

Portfolios

Low

Preference

Medium

Preference

High

Preference

ValueofTra

nsa

ctions

31

61

42

24

NumberofTra

nsa

ctions

32

49

44

29

NumberofActiveIn

vestors

35

50

43

29

PanelF:PercentilesofShock

Variable

atth

eEventDaybyFirm

Size

SizeGro

up

Epiphany1997

Epiphany1998

Epiphany1999

Epiphany2000

All

Saints’Day1999

All

Saints’Day2000

Corp

usChristi2000

Size:All

50

75

46

43

28

72

32

Size:Larg

e50

72

64

56

16

66

44

Size:Small

34

39

25

16

30

511

39

Appendix for “The Trading Volume Impact of Local Bias:

Evidence from a Natural Experiment”

Heiko Jacobs and Martin Weber∗

June 1, 2011

Abstract

This appendix contains explanations and tables that supplement the analysis in the paper. Table1 gives an overview of the data sets used. Table 2 illustrates the distribution of legally recognizedholidays across German states. Table 3 provides descriptive statistics of the stock market data. Table4 displays the distribution of industry groups across samples. Table 5 illustrates the construction ofthe factors for size, value and momentum. Table 6 provides further evidence on the level of differencesin shocks in absolute abnormal returns between holiday and non-holiday firms. Figure 1 comparesthe cumulative distribution functions of shock variables on Epiphany. Table 7 compares individualinvestor characteristics in holiday and non-holiday regions.

∗Heiko Jacobs is from the Lehrstuhl fur Bankbetriebslehre, Universitat Mannheim, L 5, 2, 68131 Mannheim. E-Mail:

[email protected]. Martin Weber is from the Lehrstuhl fur Bankbetriebslehre, Universitat Mannheim, L

5, 2, 68131 Mannheim, and CEPR, London. E-Mail: [email protected].

1

Tab

le1:

Overview

ofDataSets

Data

Data

Source

Description

Sample

Period

Dailystock

mark

etdata

Thomso

nReuters

Data

stream

Adjusted

and

unadjusted

dailyclosingprices,

mark

etcapitalization,bookvalues,

thenumberofdaily

sharestraded,th

enumberofto

talsh

aresoutsta

nding,ad-

justmentfacto

rs,th

eprimary

exch

angeaswell

asindustry

membership

forafinal

sample

of792stock

soffirm

sheadquartered

inGerm

any

June13,1988-January

15,2009

Daily

trading

record

sof

reta

il

clients

Germ

an

OnlineDiscountBro

ker

316,134stock

transa

ctionsofabout3,000individualinvestors;outofth

ese,136,125

transa

ctionsare

conducted

by2,901investors

with

regard

to965firm

sheadquar-

tered

inGerm

any

January

1,1997-April17,2001

Analyst

covera

ge

I/B/E/S

Recommendation

and

review

dates

for

ato

talof51,497

stock

buy/hold/sell-

recommendationspublish

ed

by196bro

kers,coveringabout80%

ofsa

mple

firm

s

January

1,1993

-December31,

2008

Media

covera

ge

Genios,

Factiva

126,125hand

collected

firm

-specificnewsarticlespublish

ed

inth

reeleadingGer-

man

daily

business

papers

(FinancialTim

esDeutsch

land,Sueddeutsch

eZeitung,

Handelsblatt),

coveringabout94%

ofsa

mple

firm

s

January

1,2000

-January

15,

2009

Adhocdisclosu

resandcorp

ora

te

news

Deutsch

eGesellschaft

fuer

Ad-

hoc-P

ublizitaet(D

GAP)

Tim

e-sta

mped

ad

hoc

disclosu

resforall

sample

firm

s,time-sta

mped

corp

ora

te

newsforhalf

ofsa

mple

firm

s

January

1,2000

-January

15,

2009

Intern

et

search

frequencies

for

firm

names

Google

Sta

ndard

izeddailytime-seriesofsearchfrequenciesforth

enamesofsa

mple

firm

s,

constru

cted

from

Google’s

application

“In

sights

forSearch”

January

1,2004

-January

15,

2009

Indexmembership

Deutsch

eBoerseAG

Tim

eSeriesofhisto

ricalDAX

and

MDAX

indexcomposition

June13,1988-January

15,2009

Individual

investor

chara

cteris-

tics

SAVE

study

Quantita

tiveinform

ation

on

variouseconomic

and

socio-p

sych

ologicalch

ara

cter-

isticsofabro

adly

representa

tivesa

mple

ofGerm

an

individualinvestors

based

on

arich

panelquestionnairestudy

Yearlydata

from

2005-2009

Metropolita

nregions

Initiative

Euro

pean

Metropoli-

tan

Regionsin

Germ

any

Deta

iled

inform

ation

on

thebord

ers

ofth

eeleven

Germ

an

metropolita

nareasas

defined

byth

eConferenceofM

inisters

forSpatialPlanning

Firm

headquarters

Thomso

nReuters

Data

stream,

Deutsch

eBoerseAG,firm

home-

pages

Address

offirm

headquarterlocation

2

Table

2:Legally

Recog

nized

Holiday

sin

GermanStates

This

tablesillustratesth

edistributionoflegallyauth

orizedholidaysacross

Germanstates.

Theillustrationis

constru

cted

onth

ebasisofanoverview

provided

byth

e

Ministryofth

eInterior(see

www.bmi.bund.defordetails).Holidayscovered

inth

eanalysis(E

piphany,

All

Saints’Day,

Corp

usChristi)

are

highlighted

inbold

type.

Note

that,

whileReform

ationDayis

anoth

erregionalholiday(celeb

ratedin

most

ofth

eform

erGermanDem

ocraticRep

ublic),less

than5%

ofsample

firm

sare

loca

ted

inth

eaffectedarea(see

alsofigure

1).

Holiday

Date

BW

BA

BE

BRA

BR

HA

HE

MW

PLS

NRW

RP

SAA

SAX

SAXA

SH

TH

New

Year’sDay

January

1X

XX

XX

XX

XX

XX

XX

XX

X

Epip

hany

January

6X

XX

Good

Friday

EasterSundayminus2days

XX

XX

XX

XX

XX

XX

XX

XX

EasterSunday

X

EasterM

onday

EasterSundayplus1day

XX

XX

XX

XX

XX

XX

XX

XX

MayDay

May1

XX

XX

XX

XX

XX

XX

XX

XX

Ascension

Day

EasterSundayplus39days

XX

XX

XX

XX

XX

XX

XX

XX

Whit

Sunday


Whit

Monday


XX

XX

XX

XX

XX

XX

XX

XX

CorpusChristi

EasterSunday

plu

s60

days

XX

XX

XX

Augsb

urg

erFriedensfest

August

8A

Assumption

August

15

kX

DayofGerm

an

Unity

Octo

ber3

XX

XX

XX

XX

XX

XX

XX

XX

Reform

ation

Day

Octo

ber31

XX

XX

X

All

Sain

ts’Day

Novem

ber1

XX

XX

X

DayofRepenta

nce

Wednesd

aybefore

November31

X

Christmas

December25and

December26

XX

XX

XX

XX

XX

XX

XX

XX

X=legalholiday

BW

=Baden-W

urttemberg

LS=LowerSaxony

A=only

inAugsb

urg

BA=Bavaria

NRW

=North

Rhine-W

estphalia

k=legalholidayin

predominantlycath

olicmunicipalities

BE=Berlin

RP=Rhineland

Palatinate

BRA=Bra

ndenburg

SAA=Saarland

BRE=Bremen

SAX=Saxony

HA=Hamburg

SAXA=Saxony-A

nhalt

HE=Hesse

SH=Sch

lesw

ig-H

olstein

MW

P=M

eck

lenburg

-Western

Pomera

nia

TH=Thuringia

3

Table

3:SummaryStatisticsBased

onWeekly

Data

This

table

provides

varioussu

mmary

statisticsforoursample

based

on

792German

firm

sin

theperiod

from

June13,1988to

January

15,2009.Panel

Adescribes

cross-sectionaldifferen

cesin

time-series

averages

offirm

characteristics.Firm

market

capitalization

ismea

sured

inmillion

Euro.Firm

book/market

−ratiois

the

balance

sheetvalueofth

eco

mmoneq

uityin

theco

mpany(W

orldscopeitem

03501)divided

byth

emarket

valueofeq

uity.

Firm

turnover

isth

eweekly

number

ofsh

ares

traded

scaledbyth

enumber

oftotalsh

aresoutstanding.Firm

weeksis

thenumber

ofweeksth

efirm

isin

oursample.Panel

Bdescribes

time-series

characteristics

of

weekly

cross-sectional(equally-andvalue-weighted)averages.Numberoffirmsis

thenumber

ofactivefirm

sin

oursample

inagiven

week.

Pan

elA:Cross-section

alStatistics

Variable

Mean

Med

ian

SD

5thPercentile

95th

Percentile

Firm

return

0.09

%0.16

%0.58

%-0.95%

0.67%

Firm

return

volatility

7.33

%6.37

%5.99

%2.97

%12.94

%

Firm

market

capitalization

1,14

812

34,70

417

4,190

Firm

book/market-ratio

0.52

0.44

1.36

0.09

1.04

Firm

turnover

1.42

%0.93

%1.68

%0.01%

4.55

%

Firm

weeks

556

515

322

951,075

Pan

elB:Tim

e-series

Statistics

Variable

Mean

Med

ian

SD

5thPercentile

95th

Percentile

Value-weightedreturn

index

0.16

%0.31

%2.54

%-4.22%

3.96%

Equally-w

eightedreturn

index

0.21

%0.35

%1.73

%-2.69%

2.48%

Value-weigh

tedturnover

index

2.04

%1.86

%0.98

%0.80%

3.69%

Equally-w

eigh

tedturnover

index

0.98

%0.85

%0.56

%0.33

%2.09%

Number

offirm

s41

140

915

118

3620

4

Tab

le4:

Distribution

ofIndustry

GroupsAcrossHoliday

andNon-H

oliday

Regions

This

table

showsth

eco

ncentrationandco

mpositionofindustries

(asgiven

byth

eDatastream

Lev

el2industry

classifica

tion)across

holidayandnon-holidayregionsfor

theca

seofEpiphany,

AllSaints’Day,

andCorp

usChristi,resp

ectively.

Panel

A(B

)sh

owsaneq

ual-weighted(value-weighted)analysis,

whereth

eweightofea

chindustry

groupis

determined

byth

efractionofholidayfirm

sin

thatindustry

group(byth

erelativemarket

capitaliza

tionofindustry

groupobservations).In

Panel

B,DAX

30

bluech

ips(about6%

ofobservations)

are

excluded

topreven

tasm

allfraction

oflargefirm

dominatingth

eanalysis.

Inboth

panels,

theHerfindahlindex

ofindustry

concentrationis

computedasth

esu

mofsquaredindustry

groupweights.

Industry

Epiphany

All

Saints’Day

Corp

usChristi

Holiday

Non-H

oliday

Holiday

Non-H

oliday

Holiday

Non-H

oliday

Tota

lnumberoffirm

s301

491

509

283

603

189

PanelA:Equal-W

eighted

Analysis

Herfi

ndahlindexofidustry

concentration

0.15

0.15

0.15

0.16

0.15

0.15

Oil,Gas,

Altern

ativeEnerg

y3.32%

3.67%

2.75%

4.95%

2.65%

6.35%

BasicM

aterials

5.98%

7.13%

7.27%

5.65%

6.80%

6.35%

Industrials

22.26%

21.59%

22.79%

20.14%

22.39%

20.11%

Consu

merGoods

17.28%

14.66%

16.11%

14.84%

15.92%

14.81%

Health

Care

5.65%

6.11%

4.52%

8.48%

5.31%

7.94%

Consu

merServ

ices

7.97%

8.15%

9.23%

6.01%

7.96%

8.47%

Telecommunications

0.33%

1.02%

0.98%

0.35%

0.83%

0.53%

Utilities

4.32%

2.44%

4.13%

1.41%

3.65%

1.59%

Financials

18.27%

22.20%

18.27%

25.09%

19.90%

23.28%

Tech

nology

14.62%

13.03%

13.95%

13.07%

14.59%

10.58%

PanelB:Value-W

eighted

Analysis

Herfi

ndahlindexofidustry

concentration

0.16

0.16

0.17

0.18

0.17

0.17

Oil,Gas,

Altern

ativeEnerg

y3.37%

3.79%

1.73%

4.79%

1.61%

14.41%

BasicM

aterials

8.56%

9.81%

10.06%

8.13%

10.59%

7.90%

Industrials

20.44%

20.99%

23.25%

19.22%

21.16%

16.32%

Consu

merGoods

14.11%

12.59%

12.31%

10.40%

12.64%

8.24%

Health

Care

3.52%

7.81%

3.06%

12.34%

4.37%

2.22%

Consu

merServ

ices

6.39%

7.22%

7.82%

4.63%

6.92%

7.63%

Telecommunications

1.25%

0.57%

0.93%

0.77%

0.65%

1.47%

Utilities

12.18%

3.53%

8.59%

1.57%

7.74%

3.54%

Financials

25.57%

26.59%

26.54%

31.07%

28.97%

30.97%

Tech

nology

4.61%

7.10%

5.70%

7.08%

5.34%

7.30%

5

Tab

le5:

Constructionan

dSummaryStatisticsof

Factors

forSize,

Value,

andMom

entum

Thefactors

forsize,valueandmomen

tum

are

constru

cted

from

thedailystock

retu

rnsofth

ose

firm

swhichsu

rviveth

escreen

ingprocess

asoutlined

insection3.The

screen

ingprocess

isdesigned

toex

cludevery

small

and

illiquid

stock

sand

toiden

tify

firm

swith

available

and

reliable

stock

market

data.This

procedure

resu

ltsin

afinalsample

of792firm

shea

dquartered

inGermany.

With

regard

toth

eco

nstru

ction

ofvalueand

size

portfolios,

wefollow

veryclosely

themethodologyproposed

inFamaandFrench

(1993).

Specifica

lly,

inea

chyea

ratth

een

dofJune,

weform

sixvalue-weightedportfoliosbasedonfirm

size

andeq

uitybook-to-m

arket

ratio.A

firm

’seq

uitybook-to-m

arket

ratiois

defi

ned

asth

ebalance

sheetvalueofth

eco

mmon

equityin

theco

mpany(W

orldscopeitem

03501)divided

byth

emarket

value

ofeq

uity.

Toqualify

forth

einclusion

inany

ofth

eportfolios,

afirm

hasto

meetth

efollowingrequirem

ents:1)Thestock

must

havevalid

price

data

atth

een

dof

June(i.e.nopreviousdelistingand

notbeingdisca

rded

by

thescreen

ingprocess.2)Thebook

aswellasth

emarket

valueasco

mputed

atth

een

dofDecem

ber

of

thepreviousyea

rhaveto

benon-neg

ative.

To

sort

stock

sinto

portfolios,

weform

threebook-to-m

arket

equity

groups(low,med

ium,high)based

on

the30th

and

70th

percentile

ofth

ebook-to-m

arket-ratioaswellas(indep

enden

tly)tw

osize

equitygroups(small,big)based

on

themed

ian

ofth

emarket

capitaliza

tion.From

the

intersectionsofth

esegroups,

thefollowingsixportfoliosare

constru

cted

:SmallLow,SmallMed

ium,SmallHigh,Big

Low,Big

Med

ium,Big

High.Dailyvalue-weighted

retu

rnsonth

esixportfoliosare

calculatedfrom

thebeg

inningofJuly

toth

een

dofJuneofth

enex

tyea

r.Theportfoliosare

reform

edyea

rly.

Dailyfactorretu

rnsfor

size

(SMB)and

value(H

ML)are

then

calculated

asfollows:

SM

B=

1/3(S

mallLow

+SmallM

edium

+SmallHigh)−

1/3(B

igLow

+BigM

edium

+BigHigh)and

HM

L=

1/2(S

mallHigh+

BigHigh)−

1/2(S

mallLow

+BigLow).

Withregard

toth

emomen

tum

factor,

wefollow

veryclosely

themethodologyproposedin

Carh

art

(1997).

Specifica

lly,

weform

threeeq

ually-w

eightedportfolios,

splitbyth

e30th

and70th

percentile

ofth

edistributionofth

emost

recentelev

en-m

onth

sretu

rnlagged

one

month

(i.e.th

emost

recentmonth

isskipped

).This

resu

ltsin

threemomen

tum-sorted

portfolios(W

inners,

Neu

tral,Losers),

whichare

reform

edmonth

ly.Tobeincluded

inanyofth

eportfolios,

thestock

must

havevalidprice

data

forth

ewhole

previousyea

r(i.e.nopreviousdelistingandnotbeingdisca

rded

byth

escreen

ingprocess).

Dailyretu

rnsforth

emomen

tum

factor(W

ML)are

then

calculatedasfollows:

WM

L=

Win

ner

s−Losers.

Thefollowingtable

showssu

mmary

statisticsoffactorretu

rns

basedonmonth

lydata

from

June1988to

January

2009(236observations).Month

lyfreq

uen

cyis

chosento

facilitate

comparisonwithoth

erstudies.

Thetable

displays

month

lyretu

rnsu

mmary

statisticsforth

emarket

factor(R

MRF),

thesize

factor(S

MB),

thevaluefactor(H

ML)andth

emomen

tum

factor(U

MD).

Themarket

factoris

computedasth

emonth

lyretu

rnofth

eCDAX

minusth

emonth

lyrisk-freerate.TheCDAX

isabroadGermanstock

index

withseveralhundredco

nstituen

ts.Accord

ing

toth

eindex

provider

Deu

tsch

eBoerse

Group,it

reflects

theperform

ance

ofth

eoverallGermaneq

uitymarket.

Facto

rM

ean

Std

.Dev.

t-statistic:mean=0

Min

Max

RM

RF

0.25%

5.79%

0.67

-19.90%

18.63%

SM

B-0.24%

3.50%

-1.05

-13.12%

12.77%

HM

L1.01%

2.86%

5.44

-8.76%

9.90%

WM

L0.68%

4.65%

2.26

-22.77%

14.09%

6

Table 6: Difference in Shocks in Absolute Abnormal Returns: Further Evidence

This table provides supplementary material for the test on differences in shocks in absolute abnormal returns,

as described in section 4.3 of the paper. Panel A reports differences in mean shocks in absolute abnormal

returns. To this end, daily absolute abnormal returns for each firm during t-5 to t+5, as obtained from a

German version of the Carhart (1997) four factor model, are computed. Factor loadings are estimated from

time-series regressions from t-66 to t-6. For both the holiday and the non-holiday sample, firm-specific shocks

are computed as the absolute abnormal return at t minus the average absolute abnormal return in t-5 to t+5

(excluding t). The table reports the difference between the mean shock value for the holiday sample and the

mean shock value for the non-holiday sample, averaged across years. To mitigate the effect of extreme outliers,

we winsorize the data at the 1% and 99% level before computing the mean. This is done for the holiday and

non-holiday sample in each year separately. Statistical significance is assessed by bootstrapping as described in

footnote 12. Large firms (Small firms) refer to stocks with a market value larger (smaller) than the median

stock, measured at the beginning of the year. Statistical significance at the ten, five and one percent level is

indicated by *, **, and ***, respectively. Panel B compares the frequency of extreme return events on the event

day. To this end, all holiday (non-holiday) firm-level shocks are pooled. Shock variable at least 0% means

that the idiosyncratic component of the stock’s return on the event day has at least the same importance as

on average in a nearby benchmark period (t-5 to t+5, excluding t). The odds ratio is computed as the ratio

of the fraction of extreme events for holiday firms and the fraction of extreme events for non-holiday firms.

Panel A: Difference in Mean Shocks in Absolute Abnormal Returns

Dependent Variable Epiphany All Saint’s Day Corpus Christi Pooled

Abnormal absolute return: All firms -0.08%* 0.02% -0.07% -0.04%

Abnormal absolute return: Large firms -0.09%** 0.00% -0.08% -0.06%*

Abnormal absolute return: Small firms -0.07% 0.02% -0.06% -0.03%

Panel B: Frequency of Extreme Return Events on the Event Day

Event Holiday Firm Observations Non-Holiday Firm Observations Odds Ratio

% of Observations with Shock Variable at least 0% 34.57% 37.09% 0.93



7

Figure 1: Comparison of cumulative distribution functions of shock variables on Epiphany

The following graph is intended to illustrate the economic magnitude of the difference in shock variables

between holiday and non-holiday firms (see section 4.3 of the paper). As the largest difference is observed

for Epiphany (see panel A of table 4 in the paper ), we employ the following procedure. For each year in

which Epiphany falls on a trading day, we compute the empirical cumulative probably distribution of the

shock variable for holiday firms and separately for non-holiday firms. To obtain an overall distribution, we

then average the resulting percentiles across time. This approach resembles the procedure used in the analysis

relied on in the paper, which aimed at obtaining an estimate for the shock variable of the median firm. The

following graph shows the two cumulative distribution functions. For better readability, only values above the

5th percentile and below the 95th percentile are displayed.

8

Tab

le7:

Individual

Investor

Characteristicsin

Holiday

andNon

-Holiday

Regions

Calculationsin

this

table

are

basedondata

from

theGermanSAVE

study,

arich

panel

survey

producedbyth

eMannheim

Resea

rchInstitute

forth

eEco

nomicsofAging.

Itis

designed

toprovidequantitativeinform

ation

on

theeconomic

situ

ation

and

on

relevantsocio-psych

ologicalch

aracteristics

ofarepresentativesample

ofGerman

households.

Foradetailed

descriptionofth

edesignandresu

ltsofth

eSAVE

study,

wereferth

ereader

toBoersch-Supanet

al.(2009).

Findingsreported

inth

efollowing

table

rely

ondata

gath

ered

infiveco

nsecu

tiveyea

rlyquestionnairestudieswithth

esamehouseholds,

conducted

between2005and2009.Thetable

summarizesvarious

characteristics

ofindividualinvestors

inholidayandnon-holidayregionsforea

chofourth

reeholidaysamples(E

piphany,

AllSaints’Day,

Corp

usChristi).If

notstated

oth

erwise,

thevariablesrepresentsimple

mea

nsormed

iansco

nstru

cted

from

thepooled

sample

ofall

yea

rly

survey

s.Relyingon

varioussets

ofweightingfactors

to

reca

librate

thesample

withth

eaim

ofoptimized

representativen

essdoes

notch

angeth

equalitativenatu

reofourfindings.

Theco

mputationofallvariables(w

ithth

e

exceptionof“stock

market

participation”)is

conditioned

onhouseholdswhoinvestin

thestock

market

(e.g.via

individualstock

s,butalsovia

mutu

alfunds,

REIT

Setc.).

InPanel

B,su

bjectsco

uld

givemultiple

resp

onses.

“Nodiscu

ssionoffinancialmatters”

refers

toth

efractionofsu

bjectswhostatedto

rely

neith

eronrelatives,friends,

collea

gues,neighbors

norfinancialadviserswhen

dea

lingwithfinancialmatters.

InPanel

C,th

eex

tentto

whichth

eadviseis

followed

isjudged

onascale

from

0(n

otat

all)to

10(completely).

InPanel

D,“Objectivefinancialknowledge”

reportsth

efractionofparticipants

whoansw

ered

allofth

reefinancialliteracy

questionsco

rrectly.

This

variable

isco

mputedrelyingonly

onth

equestionnaireof2009.Thequestionsth

ereinare

designed

toevaluate

knowledgewithregard

tointerest

rates,

inflationand

risk

ofinvestm

entalternatives,resp

ectively(see

www.m

ea.uni-mannheim.defordetails).“Self-assessedfinancialknowledge”

gives

resp

onden

ts’statemen

tsregard

ingth

eir

subjectivemea

sure

offinancialknowledgeonascale

from

1(verylow)to

7(veryhigh).

InPanel

E,“self-assessedrisk-taking”is

evaluatedbyaskingsu

bjectsto

assessth

e

validityofth

efollowingstatemen

tonascale

from

0(completely

false)

to10(completely

true):“Idonotmindtakingrisksin

investm

ents”.“Expectationswithregard

toownfutu

reeconomic

situ

ation”are

ratedonascale

from

0(veryneg

ative)

to10(verypositive).

9

InvestorCharacteristic

Epiphany

AllSaints’Day

Corp

usChristi

Holiday

Non-H

oliday

Holiday

Non-H

oliday

Holiday

Non-H

oliday

Panel

A:FinancialSituationandParticipationin

theStock

Market

Participationin

thestock

market?

26.35%

24.04%

25.81%

23.51%

25.80%

23.08%

Valueofstock

market

investm

ents

inEuro

(mea

n)

26,365

30,855

30,589

29,369

31,899

27,527

Valueofstock

market

investm

ents

inEuro

(med

ian)

10,000

10,000

10,000

10,000

10,000

10,000

Totalfinancialwea

lthin

Euro

(mea

n)

65,919

68,937

70,819

66,254

74,999

60,235

Totalfinancialwea

lthin

Euro

(med

ian)

36,000

32,849

32,075

34,488

34,000

33,000

Net

month

lyhousehold

inco

mein

Euro

(mea

n)

3,079

2,988

3,099

2,932

3,156

2,828

Net

month

lyhousehold

inco

mein

Euro

(med

ian)

2,800

2,600

2,800

2,550

2,800

2,500

Panel

B:Influen

ceofSocialContacts(E

xcludingProfessionalFinancialAdvisors)onFinancialMatters

Discu

ssionoffinancialmatterswithrelatives?

28.61%

28.22%

28.41%

28.21%

27.17%

29.66%

Discu

ssionoffinancialmatterswithfriends?

23.12%

24.54%

23.18%

25.12%

23.28%

25.41%

Discu

ssionoffinancialmatterswithco

llea

gues?

7.82%

8.00%

7.19%

8.60%

7.16%

8.94%

Discu

ssionoffinancialmatterswithneighbors?

0.64%

1.96%

1.37%

1.94%

1.63%

1.74%

Nodiscu

ssionoffinancialmatters?

27.45%

28.09%

28.91%

27.14%

29.37%

26.25%

Panel

C:Use

ofFinancialAdvice

Discu

ssionoffinancialmatterswithprofessionalfinancialadvisors?

50.14%

51.80%

51.19%

51.67%

50.73%

52.23%

Ifyes:Adviceseek

edatleast

once

amonth

?4.32%

2.65%

3.12%

2.88%

3.35%

2.57%

Ifyes:Adviceseek

edaboutfourtimes

per

yea

r?23.26%

24.51%

24.21%

24.28%

23.61%

25.00%

Ifyes:Adviceseek

edaboutonce

ayea

r?49.51%

50.87%

50.66%

50.54%

50.73%

50.43

Ifyes:Adviceseek

edless

thanonce

ayea

r?22.91%

21.98%

22.01%

22.29%

22.31%

22.00%

Ifyes:Towhatex

tentis

theadvicefollowed

?(m

ean,scale

from

0to

10)

6.04

6.08

5.98

6.14

6.02

6.13

Ifyes:Towhatex

tentis

theadvicefollowed

?(m

edian,scale

from

0to

10)

66

66

66

Panel

D:FinancialLiteracy

Objectivefinancialknowledge(T

opfinancialliteracy

score)

82.08%

78.59%

79.71%

79.10%

80.00%

78.59%

Self-assessedfinancialknowledge(m

ean,scale

from

1to

7)

4.97

4.93

4.89

4.97

4.91

4.97

Self-assessedfinancialknowledge(m

edian,scale

from

1to

7)

55

55

55

Panel

E:Self-AssessedRisk-T

akingin

Investm

ents

andEco

nomic

Expectations

Self-assessedrisk-takingin

investm

ents

(mea

n,scale

from

0to

10)

2.99

3.02

3.04

3.00

3.06

2.97

Self-assessedrisk-takingin

investm

ents

(med

ian,scale

from

0to

10)

22

22

22

Expectationswithregard

toownfutu

reeconomic

situ

ation(m

ean,scale

from

0to

10)

5.65

5.81

5.80

5.72

5.83

5.70

Expectationswithregard

toownfutu

reeconomic

situ

ation(m

edian,scale

from

0to

10)

66

66

66

10

the trading volume impact of local bias: evidence from a ... & weber...the trading volume impact...

Documents